\r\n\tPrevalence of reading disability among school-age children depends upon the criteria used for definition; however, the prevalence of written expression disorders in estimated to be between 5 and 12 percent, the prevalence of written expression disorders is estimated to be between 7 and 15 percent, while the prevalence of dyscalculia is estimated to be between 3 and 6 percent.
\r\n
\r\n\tRisk factors for learning disorders are family history, socio-economic conditions, prematurity, presence of other developmental, mental and health conditions (e.g. behavioral disorders, autism, attention deficit and hyperactivity disorders), prenatal exposition to neurotoxic agents, genetic disorders, particular medical conditions, history of traumatic brain injury or other neurological conditions.
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As a clinician, he has worked in different neurological departments in Italian hospitals, Alzheimer’s clinics, neuropsychiatric clinics, and neurological rehabilitative departments as the Neurological Department and Stroke Unit of Belcolle Hospital in Viterbo, Italy.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"103586",title:null,name:"Sandro",middleName:null,surname:"Misciagna",slug:"sandro-misciagna",fullName:"Sandro Misciagna",profilePictureURL:"https://mts.intechopen.com/storage/users/103586/images/system/103586.jpg",biography:"Dr. Sandro Misciagna was born in Italy in 1969. He received a degree in medicine in 1995 and another in neurology in 1999 from The Catholic University, Rome. From 1993 to 1995, he was involved in research of cerebellar functions. From 1994 to 2003, he attended the Neuropsychological department involved in research in cognitive and behavioural disorders. 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\n\t\t\t
1. Wind Turbines
\n\t\t\t
Most of the wind turbines are three-blade units (Figure 1.) [55]. Once the wind drives the blades, the energy is transmitted via the main shaft through the gearbox (supported by the bearings) to the generator. The generator speed must be as near as possible to the optimal for the generation of electricity. At the top of the tower, assembled on a base or foundation, the housing or nacelle is mounted and the alignment with the direction of the wind is controlled by a yaw system. There is also a pitch system in each blade. This mechanism controls the wind power and sometimes is employed as an aerodynamic brake. The wind turbine features a hydraulic brake to stop itself when it is needed. Finally, there is a meteorological unit that provides information about the wind (speed and direction) to the control system.
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1.1. Maintenance in Wind Turbines
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Maintenance is a key tool to ensure the operation of all components of a set. One of the objectives is to use available resources efficiently. The classical theory of maintenance was focused on the corrective and preventive maintenance [9] but alternatives to corrective and preventive maintenance have appeared in recent years. One of them is Condition Based Maintenance, which ensures the continuous monitoring and inspection of the wind turbine detecting emerging faults and organizing maintenance tasks that anticipate the failure [59]. Condition Based Maintenance implies acquisition, processing, analysis and interpretation of data and the selection of proper maintenance actions. This is achieved using condition monitoring systems [27, 28]. Thereby, CBM is presented as a useful technique to improve not only the maintenance but the safety of the equipments. Byon and Ding [14] or McMillan and Ault [50] have demonstrated its successful application in wind turbines, making the CBM one of the most employed strategies in this industry. Another example of the maintenance evolution is the Reliability Centred Maintenance. It is defined as a process to determine what must be done to ensure that any physical asset works in its operating context [71]. Nowadays it is the most common type of maintenance for many industrial fields [25, 26] and it involves maintenance system functions or identifying failure modes among others maintenance tasks [52].
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Figure 1.
Main parts of a turbine: (1) blades, (2) rotor, (3) gearbox, (4) generator, (5) bearings, (6) yaw system and (7) tower [36].
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\n\t\t\t\t
1.2. Condition Monitoring applied to Wind Turbines
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Condition Monitoring systems operate from different types of sensors and signal processing equipments. They are capable of monitoring components ranging from blades, gearboxes, generators to bearings or towers. Monitoring can be processed in real time or in packages of time intervals. The procurement of data will be critical to determine the occurrence of a problem and determine a solution to apply. Therefore, the success of a Condition Monitoring system will be supported by the number and type of sensors used and the signal collection and processing.
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Any element that performs a rotation is susceptible of being analysed by vibration. In the case of the wind turbines, vibration analysis is mainly specialized in the study of gearboxes [48, 49] and bearings [81] [85]. Different types of sensors will be required depending on the operating frequency: position transducers, velocity sensors, accelerometers or spectral energy emitted sensors.
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Acoustic emissions (AE) describe the sound waves produced when a material undergoes stress as a result of an external force [35]. They can detect the occurrence of cracks in bearings [84] and blades [91] in earlier stages.
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Ultrasonic tests evaluate the structural surface of towers and blades in wind turbines [22] [24]. Consistent with some other techniques, it is capable of locating faults safely.
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Oil analysis may determine the occurrence of problems in early stages of deterioration. It is usually a clear indicator of the wearing of certain components. The technique is widely used in the field of maintenance, being important for gearboxes in wind turbines [47].
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Thermographic technique is established for monitoring mainly electrical components [72]; although its use is extended to the search of abnormal temperatures on the surfaces of the blades [64]. Using thermography, hot spots can be found due to bad contacts or a system failure. It is common the introduction of online monitoring systems based on the infrared spectrum.
\n\t\t\t\t
There are techniques that not being so extended, are also used in the maintenance of wind turbines. In many cases, their performance is heavily influenced by the costs or their excessive specialization, making them not always feasible. Some examples are strain measurements in blades [68]; voltage and current analysis in engines, generators and accumulators [67]; shock pulse methods detecting mechanical shocks for bearings [13] or radiographic inspections to observe the structural conditions of the [61].
The FFT converts a signal from the time domain to the frequency domain. The use of FFT also allows its spectral representation [56]. Each frequency range is framed into a particular failure state. It is very useful when periodic patterns are searched [5]. Vibration analysis also provides information about a particular reason of the fault origin and/or its severity [43]. There is extensive literature demonstrating the development of the method for rolling elements. The FFT of a function f(x) is defined as [12]:
This integral, which is a function of s, may be written as F(s). Transforming F(s) by the same formula, equation (2) where F(s) is the Fourier transform of f(x) is obtained.
There are a considerable number of publications regarding the diagnosis of faults for rolling machinery that justifies the models and patterns based on the Fast Fourier Transform. Misalignment is one of the most commonly observed faults in rotating machines, being the second most common malfunction after unbalance. It may be present because of improper machine assembly, thermal distortion and asymmetry in the applied load. Misalignment causes reaction forces in couplings that are the major cause of machinery vibration. Some authors evaluated numerically the effect of coupling misalignment and suggested the occurrence of strong vibrations at twice the natural frequency [70] [95], although rotating machinery can excite vibration harmonics from twice to ten harmonics depending on the signal pickup locations and directions [53].
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Faults do not have a unique nature and most of the time, problems on a smaller scale are linked, e.g. in the case of misalignment, when an angular misalignment is studied, parallel misalignment (minor fault) needs to be take into account. Al-Hussain and Redmond reported vibrations for parallel misalignment at the natural frequency from experimental investigations [4].
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To facilitate the diagnosis in rolling elements, some companies and researchers tabulate the most common failure modes in the frequency domain, so that the analysis can be carried out easier. Thus, the appearance of different frequency peaks determines the existence of developing problems such as gaps, unbalances or misalignments among other circumstances [31].The great advantage of these tables is that the value of the frequency peak is not a particular value and may be adapted to any situation where the natural frequency (or the rotational speed) is known.
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Wavelet transform is a time-frequency technique similar to Short Time Fourier Transform although it is more effective when the signal is not stationary. Wavelet transform decompose an input signal into a set of levels at different frequencies [77]. Wavelet transforms have been applied to the fault detection and diagnosis in various wind turbine parts.
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A hidden Markov model is a statistical model in which the system being modelled is assumed to be a Markov process with hidden states. A hidden Markov model can be considered as the simplest dynamic Bayesian network [8]. Ocak and Loparo presented the application for the bearing fault detection [57].
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They are used when a statistical study is required. In these cases, common statistical, i.e. the root mean square or peak amplitude; to diagnose faults are employed. Other parameters can be maximum or minimum values, means, standard deviations to energy ratios or kurtosis. Moreover, trend analysis refers to the collection of information in order to find a trend.
\n\t\t\t\t
There are many methods that, as happened with the techniques available for CM, are very specific and therefore they are used for very specific situations. Filtering methods, for example, are designed to remove any redundant information, eliminating unnecessary overloads in the process. Analysis in time domain will be a way of monitoring wind turbine faults as inductive imbalances o turn-to-turn faults. Other methodology, the power cepstrum, defined as the inverse Fourier Transform of the logarithmic power spectrum [92], reports the occurrence of deterioration through the study of the sidebands. Time synchronous averaging, amplitude demodulation and order analysis are other signal processing methodologies used in wind turbines.
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\n\t\t\t
2. Wavelet transform
\n\t\t\t
The wavelet transform is a method of analysis capable of identifying the local characteristics of a signal in the time and frequency domain. It is suitable for large time intervals, where great accuracy is requested at low frequencies and vice versa, e.g. small regions where precision details for a deeper processing are required at higher frequencies [23]. The wavelet transform can be defined as a signal on a temporal base that is filtered successive times and whose average value is zero. These wavelets are irregular and asymmetrical [51]. The transform has many applications in control process and detection of anomalies. It enables to analyse the signal structures that depend on time and scale, being a useful method to characterize and identify signals with spectral features, unusual temporary files and other properties related to the lack of stationary. When the frequency range corresponding to each signal is known, the data can be studied in terms of time, frequency and amplitude. Therefore it is possible to see which frequencies are in each time interval, and may even reverse the wavelet transform when it is necessary. Previously to the wavelet transform, the FFT was able to work with this type of signals in the frequency domain but without great resolution in the time domain [38].
\n\t\t\t
The wavelet transform of a function f(t) is the decomposition of f(t) in a set of functions and ψ\n\t\t\t\t\n\t\t\t\t\ts,τ\n\t\t\t\t\n\t\t\t\t(t), forming a base. It is defined as [88] [66]:
Wavelets transforms are generated from the translation and scale change from a same wavelet function ψ(t), called mother wavelet, which is given by equation (4):
where s is the scale factor, and τ is the translational factor.
\n\t\t\t
The wavelets ψ\n\t\t\t\t\n\t\t\t\t\ts,τ\n\t\t\t\t\n\t\t\t\t(t) generated from the same mother wavelet function ψ(t) have different scale s and location τ, but the same shape. Scale factors are always s>0. The wavelets are dilated when the scale s>1 and contracted when s<1. Thus, the changing of the value s can cover different ranges of frequencies. Large values for the parameter s correspond to lower frequencies ranges or a large scale for ψ\n\t\t\t\t\n\t\t\t\t\ts,τ\n\t\t\t\t\n\t\t\t\t(t). Small values of s correspond to lower frequencies ranges or very small scales.
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The wavelet transform can be continuous or discrete. The difference between them is that the continuous transform provides more detailed information but consuming more computation time while the discrete signal is efficient with fewer parameters and less computation time [17]. The Discrete Wavelet Transform coefficients are a group of discrete intervals of time and scales. These coefficients are used to formalize a set of features that characterize different types of signals. Any signal can be divided into low frequency approximations (A) and high frequency details (D). The sum of A and D is always equal to the original signal. The division is done using filters (Figure 2).
\n\t\t\t
Figure 2.
Decomposition diagram.
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To reduce the computational and mathematical costs due to duplication of data, a sub-sampling is usually performed, containing the half of the collected information from A and D but without losing information. It is common to accompany this information with a graphical representation where the original signal is divided in low pass filters and high pass filters [15]. When the signals are complex, the decomposition must be to further levels and it is not sufficient with two frequency bands. From this need, multilevel filters appear. Multilevel filters repeat the filtering process iteratively with the output signals from the previous level. This leads to the so called wavelet decomposition trees (Figure 3.) [2]. By decomposing a signal in more frequency bands, additional information is obtained. A suitable branch to each signal is highly recommended as more decompositions do not always mean higher quality results.
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Figure 3.
Wavelet decomposition tree.
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The calculation of the Continuous Wavelet Transform starts for an initial time and a scale value. The result of multiplying the two signals is integrated into the whole space of time. Subsequently, this integral is multiplied by the inverse of the square root scale value, obtaining a transformed function with a normalized energy. This process is iterative until the end of the original signal is reached and must be repeated for all the values of scale that sweep the frequency range to be studied.
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\n\t\t\t\t
2.1. Wavelet families
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The concept of wavelet has emerged and evolved during the last decades. Though new families of wavelet transforms are rapidly increasing, there are a number of them that have been established with more strength over time. In most situations, the use of a particular family is set by the application.
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Daubechies wavelets are the most used wavelets, representing the foundations of wavelets signal processing and founding application in Discrete Wavelet Transform. They are defined as a family of orthogonal and smooth basis wavelets characterized by a maximum number of vanishing moments. The degree of smoothness increases as long as the order is higher. Daubechies wavelets lead to more accurate results in comparison to others wavelet types and also handle with boundary problems for finite length signals in an easier way [58] [29] [60] [94]. Wavelets have not an explicit expression except for order 1, which is the Haar wavelet. The inability to present a wavelet equation by a particular formula will be the general trend for almost all types of wavelet families [76].
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As above mentioned, Haar wavelets are Daubechies wavelets when the order is 1. They are the simplest orthonormal wavelets. The main drawback for Haar wavelets is their discontinuity as a consequence of not solving breaking points problems for its derivates. The Haar transform is one of the earliest examples of a wavelet transform and it is supported by a function is an odd rectangular pulse pair [33]. Haar functions are widely used for applications as image coding, edge extraction and binary logic design and are defined as [46] [41] [34] [30]:
The main advantages of the Haar wavelet are its accuracy and fast implementation compared with others methods, its simplicity and small computational costs, and its capacity for solving boundaries problems [87].
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Symlet wavelet transform is an orthogonal wavelet defined by a scaling filter (a low-pass finite impulse response filter of length 2N and sum 1). Symlet wavelet transform is sometimes called SymletN, where N is the order. Symlet wavelets are near symmetric. Furthermore, they have highest number of vanishing moments for a given width [7].
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Coiflet wavelets are a family of wavelets whose main characteristics are similar to the Symlet ones: a high number of vanishing moments and symmetry. Coiflet family is also compactly supported, orthogonal and capable to give a good accuracy when the original signal has a distortion. The Coiflet wavelets are defined for 5 orders [18].
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Biorthogonal wavelets have become very popular because of its versatility, being capable of supporting symmetric or antisymmetric signals. They perform very well under certain boundaries conditions [97]. Moreover the Biorthogonal wavelet transform is an invertible transform. They have two sets of lowpass filters for reconstruction, and highpass filters for decomposition [32].
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Along with the Haar wavelets, the Meyer family is one of the exceptions that can be represented by an equation. The Meyer wavelets have numerous applications in the theory of functions, solving differential equations, signal processing, etc. [39]. Meyer family has not compact support being this one of its drawbacks. It is defined by equation (6) [44]:
where \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tθ\n\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\tω\n\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t is a continuously and differentiable function equal to \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tπ\n\t\t\t\t\t\t\t\t\t4\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t for\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tω\n\t\t\t\t\t\t\t\t≥\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tπ\n\t\t\t\t\t\t\t\t\t3\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t.
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2.2. Wavelet transform applications
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The use of the wavelet transform has been developed over the past two decades focused on the process diagnosis and instrumentation. In 1990, Leducq introduces them in the analysis of hydraulic noise for a centrifugal pump [45]. Later other authors demonstrates its usefulness for the detection of mechanical failures and the health monitoring control in gears [74] [11] [90] [21] [82] [80]. Cracks in rotors [1], structures [73] [63] [89] [10] or composite plates [75] has been another exploitation source for wavelet transforms. In 1994, Newland researches on their properties and applications, and coins the term harmonic wavelet. Harmonic wavelets are used for ridge and phase identification in signals [54]. The results showed that the cracks found reduced the rotor speed. The effectiveness of wavelets has also been compared with the envelope detection methodology in the diagnosis of faults in the bearings, obtaining results in shorter time analysis [85].
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Due to its good analytical skills in time regarding the frequency, wavelet transform is a guarantee of success in the study of transient processes. Chancey and Flowers [16] managed to discover a relation between vibration patterns and the coefficients of a wavelet. Kang and Birtwhistle [40] or Subramanian, Badrilal and Henry [78] developed techniques to find problems in power transformers. Yacamini [96] proposed a method to detect torsional vibrations in engines and generators from the stator currents.
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At present, the development of techniques associated to the scopes mentioned previously are still being implemented but others wavelet transforms purposes are emerging, such as classification of linear frequency modulation signals for radar emitter recognition [83] or applications to damages caused by corrosion in chemical process installations [86]. As follow there is an explanation for some of the most examined in the scientific literature.
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The application of wavelets transforms in wind turbines focuses on the implementation of adaptive controllers for wind energy conversion systems. Wavelet transform is capable of providing a good and quick approximation. The drivers studied under different noise levels achieved higher performances [69]. Other works study the monitoring and diagnosis of faults in induced generators with satisfactory results. In these cases a combination of DWTs, accompanied by statistical data and energy is proposed. The use of decomposed signals spectral components is other highly interesting technique of study. Its harmonic content has suitable characteristics to be employed in fault diagnosis as an alternative to conventional methods [3].
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Rolling bearing plays an important role in rotating machines. The choice of a particular wavelet family is crucial for the maintenance and fault diagnosis. The location of peaks on the vibration spectrum can identify a particular fault. Wavelet decomposition trees are a useful tool for this identification. The mean square error extracted from the terminal nodes of a tree reports the failure and its size [17]. There are also studies focused on determining what type of wavelet is suitable for bearing maintenance [79].
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The wavelet transform is a good signal analysis method when a variation of time but not of space exists. The analysis provides information about the frequency of the signal, being a solution for the engine failure detection. There are detection algorithms that identify the presence of a fault in working condition and are ahead of the shutdown of the system, reducing costs and downtimes [19] [20]. These algorithms are independent of the type of engine used. Other studies in this field, present methods to detect imbalances in the stator voltage of a three phase induction motor. The wavelet transform of the stator current is analysed. Computationally, these methods are less expensive than other existing and can detect faults in an early stage. In the same vein, monitoring fatigue damage has been studied [65].
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3. Condition Monitoring for engine-generator mechanism
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A novel approach for Condition Monitoring based on wavelet transforms is introduced. A system for a mechanism based on an engine and a generator will be shown. It has been designed to represent any similar mechanism located in a wind turbine, generally in the nacelle. These mechanisms are used in cooling devices (generators, gearboxes), electric motors for service crane, yaw motors, pitch motors (depending on the configuration) or pumps (oil, water) according to the sub systems configurations, ventilators, etc (Figure 4).
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A set of faults are induced in different experiments: ski-slope faults, misalignment faults, angular misalignment faults, parallel misalignment faults, rotating looseness faults and external noise faults. Pattern recognition is obtained from the extraction of vibration and acoustic signals. A Fault Detection and Diagnosis method is developed from the patterns of these signals. In order to recognize the patterns, three basic steps have been followed [37]:
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The data acquisition on the testing bench (Figure 5).
The extraction of the features of the experiment using specific algorithms.
A decision-making.
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A classification has been done to obtain the optimal pattern recognitions employing the data from Fast Fourier Transform and wavelet transforms applied to the vibrations and sounds signals respectively.
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Figure 4.
Different locations of a wind turbine where the CM can be used: (1) fans, (2) gear oil pump, (3) oil pump for brake and (4) water cooling pump.
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3.1. Case study
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The experiments were made on a mechanism consisting of an engine and a generator linked by an elastic coupling joint. The sensors employed were a current sensor, an ambient temperature sensor, another temperature sensor located in strategic points of the mechanism, a vibration sensor; and a sound sensor (microphone). The data obtained by these sensors are stored in a data acquisition board, except for the vibration which is collected directly with a vibrometer. The software employed was LabView and specific software for vibration provided by the manufacturer Kionix. The speed of the engine and its associated frequency were set by a frequency variator, and the energy is dispelled using a resistive element.
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The allocation of the vibration measurements were: two points for the engine and two for the generator. Points of selection were located at the end of each machine and as close as possible to the axis which is the main rotational element of the mechanism (Figure 6).
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Figure 5.
Experimental mechanism.
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Figure 6.
Measuring points.
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The experiments were completed for an average time of 10 seconds each one, and every experiment was repeated 3 times. Therefore, for each experiment 12 measurements of temperatures, currents, sound, velocities and vibrations were taken (Figure 7). In the case of vibration, the vibrometer is capable of storing samples for the ‘x’, ‘y’ and ‘z’ axis, in addition to a total measurement for the point studied (Figure 8).
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The experiments were carried out in order to identify couplings and misalignments in different degrees. The engine has 4 rubber clamping (silemblocks), while the generator has 3 rubbers clamping. The silemblocks were located at the ends, having two on the right side of the engine and two on the left side. The generator has them placed in a triangle, two in the area closest to the coupling and one at the end. The first experiment recorded under free fault conditions, and the rest of experiments were performed when the silemblocks were removed from the engine and the generator in order to create the different degrees of decoupling (Figure 9).
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Figure 7.
Data collection in LabView.
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Figure 8.
Data collection with Kionix software (vibration).
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The rotational speed is 1500 rpm, i.e. 25 Hz. In order to do an analysis above the natural frequency, the number of samples was increased from 25 Hz to 125 Hz, being 25 Hz the default samples. This guarantees a range 5 times bigger than the natural frequency of the engine.
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\n\t\t\t\t\t\t\t\tExperiment\n\t\t\t\t\t\t\t
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\n\t\t\t\t\t\t\t\tType of experiment\n\t\t\t\t\t\t\t
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\n\t\t\t\t\t\t\t\tData set\n\t\t\t\t\t\t\t
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1
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Free fault conditions
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From 1 to 12
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2
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Misalignment removing silemblocks from the right side of the engine
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From 13 to 24
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3
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Misalignment removing silemblocks from the right side and the front left one of the engine
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From 25 to 36
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4
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Generation of resistance in the coupling
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From 37 to 48
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5
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Misalignment removing the silemblock from the right side of the generator
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From 49 to 60
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6
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Misalignment removing 2 silemblocks near to the coupling in the generator
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From 61 to 72
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7
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Misalignment removing the silemblock from the right side of the generator and one from the left side of the engine
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From 73 to 84
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8
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Use of a rigid coupling
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From 85 to 96
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Table 1.
Experiments (1500 rpm).
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The FFT of each signal has been developed in Matlab. An algorithm that allows the comparison of two signals for a given frequency was created. The main purpose is to compare pattern conditions with the signals of the rest of experiments that represent a fault and to analyse the peaks found in the natural frequency and its multiples. In some cases it is important to analyse the area located below the natural frequency. Another advantage of the program is that it is possible to obtain the amplitude values for a certain frequency range (Figure 10). With a click on a particular peak, the program provides the data.
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Figure 9.
Misalignments induced removing silemblocks from the engine and the generator and experimentation with a rigid coupling.
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Values for 25 Hz (natural frequency or 1X), 50 Hz (2X), 75 Hz (3X) and 100 Hz (4X) have been taken into account. Frequencies above these values have been discarded.
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Figure 10.
FFT of a vibration signal.
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3.2. Vibration diagnosis and results
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The most common spectrums for engine-generator mechanisms are presented. Examples based on the experiments held are shown.
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\n\t\t\t\t\tSki-slope fault\n\t\t\t\t
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A ski-slope fault appears when the spectrum begins at a high level and then it goes down slowly (Figure 11). A ski-slope shows a problem with the quality of the sensor. Sometimes it happens because the sensor has experienced a transient during the measurement process. The transient may be mechanical, thermal or electrical.
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\n\t\t\t\t\tMisalignment faults\n\t\t\t\t
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Misalignment fault appears when the centrelines of coupled shafts do not coincide. If the misaligned shaft centrelines are parallel but not coincident, then the misalignment is a parallel misalignment. If the misaligned shafts meet at a point but they are not parallel, the misalignment is angular. Most of the cases are a combination of them. The diagnosis is based on dominant vibration from the natural frequency (1X) at twice the rotational rate (2X), with increased rotational rate levels (3X, 4X, etc.) acting in the axial, vertical or horizontal directions.
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\n\t\t\t\t\tAngular misalignment fault\n\t\t\t\t
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Angular misalignment fault produces a bending moment on both shafts and this generates a strong vibration at 1X, and some others at 2X and 3X for the axial direction. There will also be strong radial components for vertical and horizontal directions (Figure 11).
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\n\t\t\t\t\tParallel misalignment fault\n\t\t\t\t
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Parallel misalignment fault produces a shear force and a bending moment on the coupled end of each shaft. High vibration levels at 2X as well as 1X are produced in the radial direction. Most often the 2X component is higher than 1X. Depending on the coupling, there can be 3X or 4X, even reaching 8X when the misalignment is severe (Figure 11).
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\n\t\t\t\t\tRotating looseness fault\n\t\t\t\t
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Rotating looseness fault will create harmonics or sub-harmonics every 0.5X. Even 1/3 order harmonics are possible (Figure 11).
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\n\t\t\t\t\tExternal noise fault\n\t\t\t\t
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It is very common to find a peak in a spectrum that is difficult to analyse. This happens because of the vibration from another machine or process. The peak will typically be at a non-synchronous frequency (Figure 11). External noise can be verified stopping the machine (or varying the speed) and seeing if the vibration is still present or checking local machines for the same frequency source.
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Figure 11.
a) Angular misalignment fault (red) and pattern condition (blue), (b) parallel misalignment fault (red) and pattern condition (blue), (c) ski-slope fault (blue) and pattern condition (red) and (d) rotating looseness (blue); and external noise (red).
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3.3. Vibration results
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As a rule, the natural frequency (1X) has been kept as the reference. Following the same nomenclature, the peaks at 50 Hz, 75 Hz and 100 Hz have been named 2X, 3X and 4X.
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Figure 12.
Vibration for point 1.
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Figure 13.
Vibration for point 2.
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Vibration patterns are different for the four operating points. It has been detected that the natural frequency, regardless of its amplitude, tends to predominate in the experiments associated with the end points of the set (Figures 12 and 15). Additionally, the generator’s closest point to the coupling also has a similar pattern (Figure 14). The second point differs from the rest, yielding most predominant peaks from the frequency at 50 Hz (Figure 13). To make the vibration analysis, it must be taken into consideration not only the appearance of peaks, but also the amplitude. The same diagnosis for two experiments can vary its amplitude depending on the severity of the faults found. The main symptoms appear when peaks at 0.5X, 1X, 2X and 3X, sidebands and noise sources are detected. When a failure is studied at an advanced stage, peaks at 4X are noticeable (case of rigid coupling).
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Figure 14.
Vibration for point 3.
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Figure 15.
Vibration for point 4.
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The diagnosis of the experiments reveals that the mechanism has a minor looseness which causes the appearance of a high peak at the natural frequency in some cases, even under free fault conditions. This looseness appears because the engine and the generator are not anchored directly to the test bench. The assembly was done on a surface that has facilitated the removal of the silemblocks when the experiments required it, e.g. to create different degrees of misalignment. On the other hand, this action expands the vibration intentionally because in this way it is closer to the actual behavior of the nacelle. These frequency peaks change their trend in 1X as long as the study advances from the end of the engine to the generator. From point 2, the peak at frequencies as 2X and 3X becomes more significant and sometimes exceed the amplitude of the natural frequency.
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The results for experiment 8 are also remarkable. The rigid coupling added causes a severe looseness and vibration. The growth of a frequency at 4X and a constant noise over the spectrum is observed. Although it is usual to find sidebands, peaks below 1X and high frequency peaks for all this type of experiments, this feature is unique to this last experiment. Initially, a similar diagnosis for cases 1, 4 and 8 was expected, but the behavior has been slightly different for this reason.
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3.4. Wavelet transform processing approach and results
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Wavelet transforms were employed to analyse the sound signals. As for the Fast Fourier Transform, an algorithm has been written with Matlab. This program plots and compares two signals. Data has been transformed in 5 decompositions named a\n\t\t\t\t\t\n\t\t\t\t\t\t4\n\t\t\t\t\t\n\t\t\t\t\t,\n\t\t\t\t\td\n\t\t\t\t\t\n\t\t\t\t\t\t4\n\t\t\t\t\t, d\n\t\t\t\t\t\n\t\t\t\t\t\t3\n\t\t\t\t\t, d\n\t\t\t\t\t\n\t\t\t\t\t\t2\n\t\t\t\t\t and d\n\t\t\t\t\t\n\t\t\t\t\t\t1\n\t\t\t\t\t, where each of them has an energy rate associated from the original signal (Figure 16). The algorithm also returns a percentage value per decomposition. These values of energy, the decomposition levels attached and the peak amplitudes are examined in order to look for patterns.
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Functions in the time domain can be represented as a linear combination of all frequency components present in a signal, where the coefficients are the amount of energy provided by each frequency component to the original signal. The main decomposition is associated with a\n\t\t\t\t\t\n\t\t\t\t\t\t4\n\t\t\t\t\t (main or mother wavelet) that usually has the highest energy, though it is not always necessarily the case. It has a similar pattern to the original signal. The first (d\n\t\t\t\t\t\n\t\t\t\t\t\t4\n\t\t\t\t\t), second (d\n\t\t\t\t\t\n\t\t\t\t\t\t3\n\t\t\t\t\t), third (d\n\t\t\t\t\t\n\t\t\t\t\t\t2\n\t\t\t\t\t) and fourth (d\n\t\t\t\t\t\n\t\t\t\t\t\t1\n\t\t\t\t\t) transformed signals have decreasing energy rates, being s the original signal. Usually a\n\t\t\t\t\t\n\t\t\t\t\t\t4\n\t\t\t\t\t is the low frequency component of the original signal while d\n\t\t\t\t\t\n\t\t\t\t\t\ti\n\t\t\t\t\t is the high frequency component, having d\n\t\t\t\t\t\n\t\t\t\t\t\t1\n\t\t\t\t\t the biggest value.
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It is necessary to verify that the experiments performed at 1500 rpm can be extrapolated to other speeds. In the case of wind turbines, most of the engines rotate at speeds close to 3000 rpm. A certain number of tests were done varying from 500 to 3000 rpm (at intervals of 500 rpm) in order to ensure the existence of the proportional pattern.
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The results showed that regardless of the speeds or the points of study, all the graphical representations for the different decompositions of energy had the same patterns. Figure 17 indicates the existence of a similar behavior where only changes the numerical value. The biggest ones will correspond to the main signals, while the results for decompositions d\n\t\t\t\t\t\n\t\t\t\t\t\t1\n\t\t\t\t\t and d\n\t\t\t\t\t\n\t\t\t\t\t\t2\n\t\t\t\t\t are similar.
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Data can be studied according to the evolution of a single point along the different experiments or analysing the evolution of the set points for all the experiment. Each row in Figure 18 contains two graphics, one with the amplitude peaks (left) and the other one with the energy distribution of the sound signal (right). The first two graphics correspond to the engine end (point 1). The following two graphics are the closest to the coupling (point 2). The third row belongs to the points of the generator next to the coupling (point 3), and finally, the last two graphics are for the end of the generator (point 4).
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Figure 16.
Wavelet decompositions.
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Figure 17.
Energies at different rotational speeds.
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Figure 18.
Evolution of the frequency peaks and wavelet energy decompositions for each point in experiment 2.
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Based on the distribution of the energy among the 5 different decompositions of every signal, the energy distribution for point 1, end of the engine-generator set is ruled by an almost similar pattern where each experiment has a maximum of energy in the main signal and a minimum for decomposition d\n\t\t\t\t\t\n\t\t\t\t\t\t1\n\t\t\t\t\t or d\n\t\t\t\t\t\n\t\t\t\t\t\t2\n\t\t\t\t\t. It means that by performing a decomposition of the signal, the energy has a closest resemblance to the original value, often exceeding 85% of the total energy, remaining a residual percentage for d\n\t\t\t\t\t\n\t\t\t\t\t\t1\n\t\t\t\t\t or d\n\t\t\t\t\t\n\t\t\t\t\t\t2\n\t\t\t\t\t. When the experiments are closer to the generator (points 2, 3 and 4), the energy is distributed among the 5 decompositions and not concentrated in the mother wavelet, as it is for point 1.
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All the decompositions have been registered with their energy maximum and minimum values and their patterns distribution. An example for 2 experiments is shown in Table 2.
Experiment A is associated to point 2, belonging to the engine and situated close to the coupling. Experiment B, however, is related to point 1, left end of the assembly. Experiment A has the maximum percentage of energy in d\n\t\t\t\t\t\n\t\t\t\t\t\t1\n\t\t\t\t\t and the minimum in d\n\t\t\t\t\t\n\t\t\t\t\t\t4. Furthermore, the experiment B has its maximum in the main signal and the minimum located in d\n\t\t\t\t\t\n\t\t\t\t\t\t1\n\t\t\t\t\t. The maximum-minimum patterns are d\n\t\t\t\t\t\n\t\t\t\t\t\t1\n\t\t\t\t\t\n\t\t\t\t\t-d\n\t\t\t\t\t\n\t\t\t\t\t\t4\n\t\t\t\t\t and main-d\n\t\t\t\t\t\n\t\t\t\t\t\t1\n\t\t\t\t\t respectively. Numerically, the most compensated distribution of energy is close to the coupling (experiment A – point 2) above mentioned. The patterns main-d\n\t\t\t\t\t\n\t\t\t\t\t\t1\n\t\t\t\t\t and main-d\n\t\t\t\t\t\n\t\t\t\t\t\t2\n\t\t\t\t\t appear for all the cases in point 1. However, the same maximum-minimum distribution is smaller for the points 2, 3 and 4. Unlike in point 1, there are different patterns for the 8 experiments in these points. Figures 19, 20, 21 and 22 represent the numerical values of the energy per point and experiment. It must be noted that the numerical values are higher or lower, depending on the type of experiment.
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Figure 21.
Energy values for point 3.
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Figure 22.
Energy values for point 4.
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4. Conclusions
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Wind turbines are complex systems that require a high level of reliability, availability, maintainability and safety. This chapter is focused on to guarantee these correct levels for mechanisms used in cooling devices for generators and gearboxes, electric motors for service crane, yaw motors, pitch motors, pumps, ventilators, etc.
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The mechanism brake of the engine has been simulated linking a generator by a coupling joint. The signals collected have been:
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Vibration.
Sound.
Current.
Temperature.
Velocity.
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The experiments have been done in working conditions for different points of the mechanism and considering the following failures:
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Misalignment removing silemblocks from the right side of the engine.
Misalignment removing silemblocks from the right side and the front left one of the engine.
Induction of resistance in the coupling.
Misalignment removing the silemblock from the right side of the generator.
Misalignment removing 2 silemblocks near to the coupling in the generator.
Misalignment removing the silemblock from the right side of the generator and one from the left side of the engine.
Using a rigid coupling.
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A fault detection and diagnosis model based on the Fast Fourier Transform applied to the vibration signals; together with the wavelet transform applied to sound signals has been developed. The model detects and diagnoses correctly 100% of the failures considered.
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It has been observed that for the outer ends of the engine and the generator, the appearance of a pronounced peak amplitude at the natural frequency or 2X (vibration) was associated to the maximum energy values for the main signal, the most suitable with the original, and minimum values for decomposed signals d\n\t\t\t\t\n\t\t\t\t\t1\n\t\t\t\t and d\n\t\t\t\t\n\t\t\t\t\t2\n\t\t\t\t (sound). In contrast, the results obtained close to the coupling did not follow a clear trend as the results were conditioned by the type of experiment. The numerical values of each peak were also taken into account in the establishment of the pattern recognitions, being different for each experiment. The same conclusion was reached for the energy values. Different models and results were expected because the objective was not to find similar patterns between different experiments, and the tests were never performed under identical conditions. The objective was to have different vibration patterns and their associated sound models in order to create a catalogue of possible scenarios for predictive maintenance in the mechanisms. Thus, it is possible to extend the range of possibilities to relate the result of an acoustic signal with the frequency domain using the Fast Fourier Transform.
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\n\t\n',keywords:null,chapterPDFUrl:"https://cdn.intechopen.com/pdfs/40445.pdf",chapterXML:"https://mts.intechopen.com/source/xml/40445.xml",downloadPdfUrl:"/chapter/pdf-download/40445",previewPdfUrl:"/chapter/pdf-preview/40445",totalDownloads:2847,totalViews:450,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,dateSubmitted:"April 18th 2012",dateReviewed:"August 8th 2012",datePrePublished:null,datePublished:"January 16th 2013",dateFinished:"October 23rd 2012",readingETA:"0",abstract:null,reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/40445",risUrl:"/chapter/ris/40445",book:{slug:"digital-filters-and-signal-processing"},signatures:"Fausto Pedro García Márquez, Raúl Ruiz de la Hermosa González- Carrato, Jesús María Pinar Perez and Noor Zaman",authors:[{id:"22844",title:"Prof.",name:"Fausto Pedro",middleName:null,surname:"García Márquez",fullName:"Fausto Pedro García Márquez",slug:"fausto-pedro-garcia-marquez",email:"faustopedro.garcia@uclm.es",position:null,institution:{name:"University of Castile-La Mancha",institutionURL:null,country:{name:"Spain"}}},{id:"155699",title:"Dr.",name:"Raul",middleName:null,surname:"Ruiz De La Hermosa Gonzalez-Carrato",fullName:"Raul Ruiz De La Hermosa Gonzalez-Carrato",slug:"raul-ruiz-de-la-hermosa-gonzalez-carrato",email:"raul.ruiz.hermosa@gmail.com",position:null,institution:null},{id:"155700",title:"Dr.",name:"Jesús María",middleName:null,surname:"Pinar Perez",fullName:"Jesús María Pinar Perez",slug:"jesus-maria-pinar-perez",email:"JesusMaria.Pinar@uclm.es",position:null,institution:null},{id:"156010",title:"Dr.",name:"Noor",middleName:null,surname:"Zaman",fullName:"Noor Zaman",slug:"noor-zaman",email:"nzaman@kfu.edu.sa",position:null,institution:null}],sections:[{id:"sec_1",title:"1. 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P.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2002\n\t\t\t\t\tApplication of RCM for safety considerations in a steel plant.\n\t\t\t\t\tReliability Engineering and System Safety.\n\t\t\t\t\t3\n\t\t\t\t\t78\n\t\t\t\t\t325\n\t\t\t\t\t334\n\t\t\t\t\n\t\t\t'},{id:"B23",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tDong\n\t\t\t\t\t\t\tY.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tShi\n\t\t\t\t\t\t\tH.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tLuo\n\t\t\t\t\t\t\tJ.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tFan\n\t\t\t\t\t\t\tG.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tZhang\n\t\t\t\t\t\t\tC.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2010\n\t\t\t\t\tApplication of wavelet transform in MCG-signal denoising.\n\t\t\t\t\tModern Applied Science.\n\t\t\t\t\t4\n\t\t\t\t\t6\n\t\t\t\t\t20\n\t\t\t\t\t24\n\t\t\t\t\n\t\t\t'},{id:"B24",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tEndrenyi\n\t\t\t\t\t\t\tJ.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tMc Cauley\n\t\t\t\t\t\t\tJ.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tSingh\n\t\t\t\t\t\t\tC.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2001\n\t\t\t\t\tThe present status of maintenance strategies and the impact of maintenance on reliability.\n\t\t\t\t\t IEEE Transaction Power System.\n\t\t\t\t\t16\n\t\t\t\t\t4\n\t\t\t\t\t638\n\t\t\t\t\t646\n\t\t\t\t\n\t\t\t'},{id:"B25",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tGarcía\n\t\t\t\t\t\t\tF. P.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tSchmid\n\t\t\t\t\t\t\tF.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tCollado\n\t\t\t\t\t\t\tJ. C.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2003\n\t\t\t\t\tA reliability centered approach to remote condition monitoring. A railway points case study.\n\t\t\t\t\tReliability Engineering & System Safety.\n\t\t\t\t\t80\n\t\t\t\t\t1\n\t\t\t\t\t33\n\t\t\t\t\t40\n\t\t\t\t\n\t\t\t'},{id:"B26",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tGarcía\n\t\t\t\t\t\t\tF. P.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tSchmid\n\t\t\t\t\t\t\tF.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tCollado\n\t\t\t\t\t\t\tJ. C.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2003\n\t\t\t\t\tWear assessment employing remote condition monitoring: A case study.\n\t\t\t\t\tWear.\n\t\t\t\t\t2\n\t\t\t\t\t255\n\t\t\t\t\t1209\n\t\t\t\t\t1220\n\t\t\t\t\n\t\t\t'},{id:"B27",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tGarcía\n\t\t\t\t\t\t\tF. 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W.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2000\n\t\t\t\t\tLocating defects of a gear system by the technique of wavelet transform.\n\t\t\t\t\tMechanism and Machine Theory.\n\t\t\t\t\t35\n\t\t\t\t\t8\n\t\t\t\t\t1169\n\t\t\t\t\t1182\n\t\t\t\t\n\t\t\t'},{id:"B83",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tSwiercz\n\t\t\t\t\t\t\tE.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2011\n\t\t\t\t\tAutomatic classification of LFM signals for radar emitter recognition using wavelet decomposition and LVQ classifier.\n\t\t\t\t\t Physical Aspects of Microwave and Radar Applications.\n\t\t\t\t\t119\n\t\t\t\t\t4\n\t\t\t\t\t488\n\t\t\t\t\t494\n\t\t\t\t\n\t\t\t'},{id:"B84",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tTan\n\t\t\t\t\t\t\tC. C.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t1990\n\t\t\t\t\tApplication of acoustic emission to the detection of bearing failures.\n\t\t\t\t\t Proceedings Tribology Conference. Brisbane.\n\t\t\t\t\t110\n\t\t\t\t\t114\n\t\t\t\t\n\t\t\t'},{id:"B85",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tTse\n\t\t\t\t\t\t\tP. W.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tPeng\n\t\t\t\t\t\t\tY. H.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tYam\n\t\t\t\t\t\t\tR.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2001\n\t\t\t\t\tWavelet analysis and envelope detection for Rolling element bearing fault diagnosis. Their effectiveness and flexibilities.\n\t\t\t\t\tASME Journal of Vibration and Acoustics.\n\t\t\t\t\t123\n\t\t\t\t\t303\n\t\t\t\t\t310\n\t\t\t\t\n\t\t\t'},{id:"B86",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tVan Dijck\n\t\t\t\t\t\t\tG.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tVan Hulle\n\t\t\t\t\t\t\tM. M.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2011\n\t\t\t\t\tInformation theory filters for wavelet packet coefficient selection with application to corrosion type identification from acoustic emission signals.\n\t\t\t\t\tSensors.\n\t\t\t\t\t11\n\t\t\t\t\t6\n\t\t\t\t\t5695\n\t\t\t\t\t5715\n\t\t\t\t\n\t\t\t'},{id:"B87",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tVenkatesh\n\t\t\t\t\t\t\tS.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tAyyaswamy\n\t\t\t\t\t\t\tS.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tHariharan\n\t\t\t\t\t\t\tG.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2010\n\t\t\t\t\tHaar wavelet method for solving initial and boundary value problems of Bratu-type.\n\t\t\t\t\tInternational Journal of Computational & Mathematical Sciences.\n\t\t\t\t\t4\n\t\t\t\t\t6\n\t\t\t\t\t286\n\t\t\t\t\t289\n\t\t\t\t\n\t\t\t'},{id:"B88",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tWang\n\t\t\t\t\t\t\tD.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tMiao\n\t\t\t\t\t\t\tQ.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tFan\n\t\t\t\t\t\t\tX.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tHuang\n\t\t\t\t\t\t\tH. Z.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2009\n\t\t\t\t\tRolling element bearing fault detection using an improved combination of Hilbert and wavelet transform.\n\t\t\t\t\tJournal of Mechanical Science and Technology.\n\t\t\t\t\t23\n\t\t\t\t\t3292\n\t\t\t\t\t3301\n\t\t\t\t\n\t\t\t'},{id:"B89",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tWang\n\t\t\t\t\t\t\tQ.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tDeng\n\t\t\t\t\t\t\tX. M.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t1999\n\t\t\t\t\tDamage detection with spatial wavelets.\n\t\t\t\t\tInternational Journal of Solids and Structures.\n\t\t\t\t\t36\n\t\t\t\t\t23\n\t\t\t\t\t3443\n\t\t\t\t\t3468\n\t\t\t\t\n\t\t\t'},{id:"B90",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tWang\n\t\t\t\t\t\t\tW. Q.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tIsmail\n\t\t\t\t\t\t\tF.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tGolnaragh\n\t\t\t\t\t\t\tM. F.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2001\n\t\t\t\t\tAssessment of gear damage monitoring techniques using vibration measurements.\n\t\t\t\t\tMechanical Systems and Signal Processing.\n\t\t\t\t\t15\n\t\t\t\t\t5\n\t\t\t\t\t905\n\t\t\t\t\t922\n\t\t\t\t\n\t\t\t'},{id:"B91",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tWei\n\t\t\t\t\t\t\tJ.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tMc Carty\n\t\t\t\t\t\t\tJ.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t1993\n\t\t\t\t\tAcoustic emission evaluation of composite wind turbine blades during fatigue testing.\n\t\t\t\t\tWind Engineering.\n\t\t\t\t\t17\n\t\t\t\t\t6\n\t\t\t\t\t266\n\t\t\t\t\t274\n\t\t\t\t\n\t\t\t'},{id:"B92",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tWismer\n\t\t\t\t\t\t\tN. J.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t1994\n\t\t\t\t\tGearbox analysis using cepstrum analysis and comb liftering.\n\t\t\t\t\t Application Note. Brüel & Kjaer. Denmark.\n\t\t\t\t\n\t\t\t'},{id:"B93",body:'\n\t\t\t\t\n\t\t\t\t\tWorld Wind Energy Association.\n\t\t\t\t\t2009\n\t\t\t\t\tWorld wind energy report 2009.\n\t\t\t\t\thttp://www.wwindea.org\n\t\t\t\t\tApril 2012\n\t\t\t\t\n\t\t\t'},{id:"B94",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tWu\n\t\t\t\t\t\t\tJ.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tLiu\n\t\t\t\t\t\t\tC.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2008\n\t\t\t\t\tInvestigation of engine fault diagnosis using discrete wavelet transform and neural network.\n\t\t\t\t\tExpert Systems with Applications.\n\t\t\t\t\t35\n\t\t\t\t\t1200\n\t\t\t\t\t1213\n\t\t\t\t\n\t\t\t'},{id:"B95",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tXu\n\t\t\t\t\t\t\tM.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tMarangoni\n\t\t\t\t\t\t\tR.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t1994\n\t\t\t\t\tVibration analysis of a motor-flexible coupling-rotor system subjected to misalignment and unbalance Part I: Theoretical model and analysis.\n\t\t\t\t\tJournal of Sound and Vibration.\n\t\t\t\t\t176\n\t\t\t\t\t5\n\t\t\t\t\t663\n\t\t\t\t\t679\n\t\t\t\t\n\t\t\t'},{id:"B96",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tYacamini\n\t\t\t\t\t\t\tR.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tSmith\n\t\t\t\t\t\t\tK. S.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tRan\n\t\t\t\t\t\t\tL.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t1998\n\t\t\t\t\tMonitoring torsional vibrations of electro-mechanical systems using stator currents.\n\t\t\t\t\t Journal of Vibration and Acoustics, Transactions of the ASME.\n\t\t\t\t\t120\n\t\t\t\t\t1\n\t\t\t\t\t72\n\t\t\t\t\t79\n\t\t\t\t\n\t\t\t'},{id:"B97",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tYang\n\t\t\t\t\t\t\tX.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tShi\n\t\t\t\t\t\t\tY.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tYang\n\t\t\t\t\t\t\tB.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2011\n\t\t\t\t\tGeneral framework of the construction of biorthogonal wavelets based on Bernstein bases: theory analysis and application in image compression.\n\t\t\t\t\t5\n\t\t\t\t\t1\n\t\t\t\t\t50\n\t\t\t\t\t67\n\t\t\t\t\n\t\t\t'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Fausto Pedro García Márquez",address:null,affiliation:'
University of Castilla-La Mancha, Spain
'},{corresp:null,contributorFullName:"Raúl Ruiz de la Hermosa González-Carrato",address:null,affiliation:'
University of Castilla-La Mancha, Spain
'},{corresp:null,contributorFullName:"Jesús María Pinar Perez",address:null,affiliation:'
'}],corrections:null},book:{id:"3198",title:"Digital Filters and Signal Processing",subtitle:null,fullTitle:"Digital Filters and Signal Processing",slug:"digital-filters-and-signal-processing",publishedDate:"January 16th 2013",bookSignature:"Fausto Pedro García Márquez and Noor Zaman",coverURL:"https://cdn.intechopen.com/books/images_new/3198.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:null,printIsbn:"978-953-51-0871-9",pdfIsbn:"978-953-51-6289-6",editors:[{id:"22844",title:"Prof.",name:"Fausto Pedro",middleName:null,surname:"García Márquez",slug:"fausto-pedro-garcia-marquez",fullName:"Fausto Pedro García Márquez"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},chapters:[{id:"40445",title:"Maintenance Management Based on Signal Processing",slug:"maintenance-management-based-on-signal-processing",totalDownloads:2847,totalCrossrefCites:0,signatures:"Fausto Pedro García Márquez, Raúl Ruiz de la Hermosa González- Carrato, 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Stoyanov, K. Nikolova and M. Kawamata",authors:[{id:"21294",title:"Prof.",name:"Georgi",middleName:null,surname:"Stoyanov",fullName:"Georgi Stoyanov",slug:"georgi-stoyanov"},{id:"21298",title:"MSc.",name:"Kamelia",middleName:null,surname:"Nikoliva",fullName:"Kamelia Nikoliva",slug:"kamelia-nikoliva"},{id:"24319",title:"Prof.",name:"Masayuki",middleName:null,surname:"Kawamata",fullName:"Masayuki Kawamata",slug:"masayuki-kawamata"}]},{id:"15189",title:"Integrated Design of IIR Variable Fractional Delay Digital Filters with Variable and Fixed Denominators",slug:"integrated-design-of-iir-variable-fractional-delay-digital-filters-with-variable-and-fixed-denominat",signatures:"Hon Keung Kwan and Aimin Jiang",authors:[{id:"21788",title:"Prof.",name:"Hon Keung",middleName:null,surname:"Kwan",fullName:"Hon Keung Kwan",slug:"hon-keung-kwan"},{id:"23569",title:"Dr.",name:"Aimin",middleName:null,surname:"Jiang",fullName:"Aimin Jiang",slug:"aimin-jiang"}]},{id:"15190",title:"Complex Coefficient IIR Digital Filters",slug:"complex-coefficient-iir-digital-filters",signatures:"Zlatka Nikolova, Georgi Stoyanov, Georgi Iliev and Vladimir Poulkov",authors:[{id:"18206",title:"Dr.",name:"Vladimir",middleName:null,surname:"Poulkov",fullName:"Vladimir Poulkov",slug:"vladimir-poulkov"},{id:"21534",title:"Dr.",name:"Georgi",middleName:null,surname:"Iliev",fullName:"Georgi Iliev",slug:"georgi-iliev"},{id:"22961",title:"Dr.",name:"Zlatka",middleName:null,surname:"Nikolova",fullName:"Zlatka Nikolova",slug:"zlatka-nikolova"},{id:"22962",title:"Prof.",name:"Georgi",middleName:null,surname:"Stoyanov",fullName:"Georgi Stoyanov",slug:"georgi-stoyanov"}]},{id:"15191",title:"Low-Complexity and High-Speed Constant Multiplications for Digital Filters Using Carry-Save Arithmetic",slug:"low-complexity-and-high-speed-constant-multiplications-for-digital-filters-using-carry-save-arithmet",signatures:"Oscar Gustafsson and Lars Wanhammar",authors:[{id:"23087",title:"Dr.",name:"Oscar",middleName:null,surname:"Gustafsson",fullName:"Oscar Gustafsson",slug:"oscar-gustafsson"},{id:"23088",title:"Prof.",name:"Lars",middleName:null,surname:"Wanhammar",fullName:"Lars Wanhammar",slug:"lars-wanhammar"}]},{id:"15192",title:"A Systematic Algorithm for the Synthesis of Multiplierless Lattice Wave Digital Filters",slug:"a-systematic-algorithm-for-the-synthesis-of-multiplierless-lattice-wave-digital-filters",signatures:"Juha Yli-Kaakinen and Tapio Saramäki",authors:[{id:"23134",title:"Dr.",name:"Juha",middleName:null,surname:"Yli-Kaakinen",fullName:"Juha Yli-Kaakinen",slug:"juha-yli-kaakinen"},{id:"23136",title:"Prof.",name:"Tapio",middleName:"Antero",surname:"Saramäki",fullName:"Tapio Saramäki",slug:"tapio-saramaki"}]}]}]},onlineFirst:{chapter:{type:"chapter",id:"65428",title:"Graphene Acoustic Devices",doi:"10.5772/intechopen.81603",slug:"graphene-acoustic-devices",body:'\n
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1. Introduction
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Since graphene have been found in 2004 [1], various kinds of graphene-based devices, including field effect transistor (FET) [2], memory [3], photodetector [4], sensor [5], have been built as its excellent structural and physical properties. However, almost no work was focused on the acoustic device in audio range because the graphene is hard to make low frequency sound through vibration due to the large-area requirement. Although the working principles of traditional sound devices are different, they are all depend on mechanical vibration of thin films, and driving the air to produce the sound. There is a common problem that the output audio spectrum of these sound devices is not flat, which is caused by the inherent center resonance of the diaphragm. Here, thermoacoustic effect is proposed to emit sound without vibration of the diagram. The conductive film itself can emit sound, which will be expected to achieve wide-band acoustic output. In 1917, Arnold’s group firstly used the 700 nm-Pt film as the source to realize thermoacoustic sound production [6]. The sound frequency of this device can reach to 40 kHZ, but the sound pressure (SP) was not high enough. With the rapid development of nanotechnology in recent years, many outstanding progresses have been made in sound source devices based on thermoacoustic effect. In 1999, Shinoda’s group have reported aluminum thin film sound device based on porous silicon substrate, and the wide output band can be implemented in 20–100 kHz [7]. Especially, the SP of the device is up to 0.1 Pa. After that, the research group in Tsinghua University realized the sound source device based on carbon nanotubes [8]. This thermoacoustic device not only can produce high sound pressure level (SPL) in a wide audio range, but also have the advantages of bending, stretching and transparency.
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However, most of thermoacoustic devices have some problems and technical limitations in material preparation, device structure and working mechanism. For example, the performance of aluminum thin film thermoacoustic device is seriously degraded because the aluminum is easily oxidized in air, and the device is rigid and non-transparent [7]. For the thermoacoustic device based on carbon nanotubes [8], 100 V is needed to drive the device due to its large square resistance (1 kΩ/□).
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For the currently existing problems of low reliability, poor performance and high driving voltage, the high-performance thermoacoustic device must meet three conditions. First, the conductor should be thin enough with a low thermal capacity per unit area (HCPUA). Second, the conductor should prevent thermal leakage from the substrate (i.e. suspend). Third, the conductor area should be large enough to build a sufficient sound field. Graphene as a kind of two-dimensional layered material provides an opportunity for the development of thermoacoustic devices due to its ultralow HCPUA. After a lot of efforts, Ren’s group has made some new achievements in graphene thermoacoustic devices. In particular, their graphene earphones and graphene throat have attracted widespread attention as their potential applications in solving the problem of human listening and speaking. Therefore, this chapter will detail the graphene sound sources, and explore the method of realization high performance devices.
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2. Establishment of theoretical model
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The physical image of thermoacoustic effect can be described as: when an alternating current signal is passing through the thin film, a film Joule heat is generated and quickly transferred to the surrounding air medium. Due to the periodic rise and fall of the surface temperature, a thin layer of air molecules on the film surface will continuously expand and contract to produce sound waves. Figure 1a shows the detailed physical process of the thermoacoustic device. The input AC signal (electric energy) is converted into Joule thermal fluctuation (thermal energy), and finally converted into sound waves (sound energy). Different from the principle of traditional acoustic devices, the conductive film itself does not vibrate itself but makes the air vibrate by heating the air medium during the process of thermoacoustic effect. Figure 1b shows the theoretical waveforms corresponding to the three kinds of energy in the time domain. Assuming a sinusoidal signal is input, the Joule heat generated is squared with the electrical signal. Both positive half-cycle and negative half-cycle electrical signals can produce positive temperature fluctuations. Therefore, the frequency of temperature changes is twice the frequency of the input electrical signal, and the frequency of the sound waves is also doubled. This is an important feature that distinguishes the thermoacoustic effect from the traditional sound production.
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Figure 1.
(a) Energy conversion process of thermoacoustic devices. (b) Theoretical waveforms during thermoacoustic effect [9].
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In order to obtain high performance graphene sound source devices, it is necessary to carry out theoretical design to guide the experiment. Firstly, the theoretical model of thermoacoustic effect is established, and the sound pressure produced by graphene is predicted theoretically. The structure of graphene/substrate/back plate is proposed in Figure 2. The heat loss from the substrate should be taken into account due to the contact of graphene with the substrate. When the acoustic frequency is low, the heat flux can penetrate the substrate to reach the backplane, but when the sound frequency is high, the heat flux cannot reach the backplane. So it needs to be discussed in two frequency bands [9, 10, 11]:
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Figure 2.
Theoretical model of the graphene sound source [10].
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when f <\n\n\n\nα\ns\n\n\n4\n\nπL\nS\n2\n\n\n\n\n, the SP generated by graphene in the far field can be expressed as:
where f is frequency of sound; \n\n\nα\ns\n\n\n and \n\n\nL\ns\n\n\n are the thermal diffusivity and thickness of substrate, respectively; γ is the heat capacity ratio of gas; \n\n\nυ\ng\n\n\n is the sound velocity in gas; each layer has e i = √ _______ K i ρ i C p,i is the thermal effusivity of material i. The subscript i represent gas (g), substrate (s), or backing layer (b), respectively. q0 is the input power density; ki, ρi and Cp,i\n are the thermal conductivity, density and specific heat capacity of each layer of material, respectively; M is a frequency related factor. Under high frequency, M ≈ 1, then the two equations are the same.
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Based on the above theoretical formula, the relationship between the SP and thickness of graphene, input power and test distance can be calculated and analyzed. Figure 3a shows the theoretical waveform of SP. It can been seen that the corresponding SP value increases with the thickness of graphene decreasing, and the SP will increase as the input power increases at the same thickness. Figure 3b shows the theoretical relationship between SPL and graphene thickness at different test distances. At the same thickness, the SPL value will decrease as the test distance increases.
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Figure 3.
(a) Theoretical relationship between SP and thickness of graphene under different input power conditions. (b) Theoretical relationship between graphene SP and thickness under different test distance conditions [8].
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Then the sound field radiation of graphene sound source device is analyzed. The sound source device can be regarded as a point sound source in the far field, and the theoretical acoustic directivity \n\nD\n\nθ\nφ\n\n\ncan be expressed as [12]:
where\n\n\nθ\n\n and \n\nφ\n\n are the parameters of the spherical coordinate system; \n\n\nk\n0\n\n=\n2\nπ\n/\n\nλ\n0\n\n\n is the wave number; Assuming that the sound source device is at the center of the sound field, \n\n\nL\nx\n\n\n and \n\n\nL\ny\n\n\n are the length and width of the sound source, respectively. Figure 4 shows the directivity of the graphene point sound source at different sound frequencies. When the sound frequency is lower, the corresponding acoustic wave is longer, and the angle of sound field coverage is lower. With the increase of acoustic frequency, the acoustic wavelength decreases, and the sound field becomes more concentrated in the smaller angle perpendicular to the sample direction, that is, the directivity is enhanced.
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Figure 4.
Directivity distribution of graphene sound source at different sound frequencies [9]. (a) 10 kHz, (b) 16 kHz, (c) 20 kHz, (d) 30 kHz, (e) 40 kHz, (f) 50 kHz.
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Then, the sound field distribution of graphene device is analyzed, which includes near field and far field. Firstly, the distance of Rayleigh is defined as A/λ, where A and λ are the area and wavelength of graphene, respectively. The acoustic wave propagates in the form of plane wave when the test distance is smaller than Rayleigh distance in near field. However, when the measured distance is larger than Rayleigh distance, the acoustic wave propagates in the form of spherical waves in far field.
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For \n\nr\n<\n\nR\n0\n\n\n, the SP in near field can be expressed as:
Based on the above two formulas, the theoretical distribution of graphene sound field can be simulated.
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In order to fully consider the influence of the actual size of the device on the sound field distribution, the finite element software Comsol is used to simulate the sound field distribution. Figure 5 shows the sound field distribution of graphene sound source devices simulated by finite element software. The simulation results show that the SP distribution on the surface of the device is stronger and the angle of sound coverage is wider. However, the sound coverage angle obtained by simulation is narrower when the point sound source approximation is used (Figure 4).
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Figure 5.
SP distribution of graphene sound source device obtained by finite element simulation [9].
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3. Graphene-on-paper sound source devices
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The fabrication process of the multilayer graphene sound source device is introduced in Figure 6. Firstly, multilayer graphene was prepared on nickel foil by CVD method, the thickness of which was about 20 nm. Then, the graphene film of 1 cm × 1 cm was transferred to filter paper. The transfer process was as follows: the FeCl3 solution was used to etch graphene with nickel substrate. After nickel was etched, graphene was transferred to deionized water, and finally graphene was filled with porous filter paper. In the transfer process, filter paper with the pore size of 30–50 μm was used as substrate. Because the graphene film can be suspended on larger pores, which can effectively reduce heat loss to substrate and improve the efficiency of sound production. In order to test the sample acoustically, the sample was installed on the PCB board and the two electrodes were made. The Ag was used as electrode material of graphene sound source device. Figure 6 shows the schematic diagram of a graphene-on-paper sound source device. When the sound frequency electric signal is applied to graphene device, the air near its surface can be heated, and then the periodicity of air vibration can induce sound waves.
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Figure 6.
A process for preparing a multilayer graphene sound source device: (a) growing a multilayer graphene on nickel; (b) transfer to filter paper by wet method; (c) prepare the Ag electrode at both ends of the graphene; (d) mount the device to the backplate and extract the signal [9].
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Figure 7a shows the scanning electron microscope (SEM) image of the graphene. There are some ripples in graphene to get regular graphics. The oxygen plasma can be used to pattern graphene film. Figure 7b shows the image of the graphene film after oxygen plasma treatment. Figure 7c shows the Raman peak of the sample obtained from the corresponding colored spots in Figure 7b.
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Figure 7.
(a) SEM image of graphene. (b) An optical image of the graphene after oxygen plasma etching. (c) Raman spectrum in the range of 1200–2800 cm−1 of the graphene. The red line and green line correspond to the red and green part line in Figure 7b, respectively [9].
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It can be seen that the two strong peaks locate at 1582 and 2700 cm−1, corresponding to G and 2D bands, respectively. The sample exhibits typical multilayered graphene characteristic with a strong G peak and a broad 2D peak (green line), while some spots exhibit monolayer graphene feature with a sharp G peak and a single higher 2D peak (red line). The small intensity of D-band observed at 1350 cm−1 indicates the low levels of defects and local-disorders in the deposited films.
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Three paper-based graphene sound source devices are fabricated, named sample 1, sample 2 and sample 3, and their resistance are 32, 143, and 601 Ω, respectively. The area of these devices is 1 cm × 1 cm, and the average thickness of three graphene sheet samples are about 100, 60, and 20 nm, respectively. The acoustics test platform is composed of a signal generator, a standard microphone and a dynamic frequency analyzer, as shown in Figure 8a. The signal generator drives the graphene sound source device to produce sound, and the sound wave is received by the standard microphone. Finally, the sound wave is analyzed by the dynamic frequency analyzer and the frequency is converted from time domain to frequency. The graphene sound source is directly tested by using a standard microphone (Figure 8b). The distance from the sound source to the microphone is 5 cm. The relationship between output SP and the input power is shown in Figure 8c. The result indicates that the SP increases linearly with increasing input power. Figure 8d shows the relationship between the SP and the test distance. For the graphene sound source devices, the Rayleigh distance can be calculated as 4.7 × 10−3 m. The test distance of SP is ranging from 1 to 10 × 10−2 m, which belongs to the far-field. The SP decreases with the increase of the test distance at 16 kHz sound frequency, indicating an inverse proportional to distance. This result is in agreement with the estimate of far-field. When the measure distance is 5 cm at 16 kHz sound frequency, the omnidirectional dispersion patterns can be achieved by testing the change of the SP with the receiving angle. The sound field directivity of multilayer graphene is shown in Figure 8e. The SP is mainly concentrated within the ±30° of the positive axis. After this angle, the SP is obviously attenuated. Figure 8f shows the relation between the output SPL and the frequency. The frequency is ranging from 3 to 50 kHz. The three curves are normalized with the same power density (1 W/cm2) at different thickness of the graphene film. It can be seen that the 20 nm graphene film has the highest SPL value because of its lowest HCPUA. The 60 and 100 nm graphene rank second and third, respectively. It indicates that thinner graphene sheets can produce higher SPL. The sound frequency band can cover audible and ultrasound. Especially in ultrasound range 20–50 kHz, there exists flat frequency response.
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Figure 8.
The acoustic test platform and test results of graphene sound source [10]. (a) Schematic diagram of test platform. (b) Onsite photo of the experimental setup. (c) The output SP from graphene versus the input power. (d) The plot of the output SP of graphene versus the measurement distance. (e) Directivity of the graphene sound source in far-field. (f) The output SPL versus the frequency. The three curves are normalized with the input power 1 W/cm2.
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The theoretical model has been introduced in the previous section. Figure 9 shows the sound radiation of the graphene sound source in far-field. The on-axis direction has the largest sound intensity, the sound intensity decreases with the angle and the main intensity area focuses on axis ±30 angles. Those results are agreed with the experimental directivity as shown in Figure 8e.
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Figure 9.
Theoretical half-space directivity of the graphene sound source in far-field [10].
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The physical scene of thermoacoustic effects is depicted in Arnold and Crandall’s research results. When thermoacoustic device is working, alternating electrical signals generate joule heat through conductors. It would heat up the air near its surface and then the SP is generated by the changing of the air temperature. To verify this physical phenomenon through experiments, the advanced infrared thermal imaging instrument is used to investigate to the relations between the surface temperature distribution of graphene and the amplitude of input power. The input power q0 is increasing from 0 to 0.16 W, the temperature distributions are collected in Figure 10a–h. The relationship between input power and graphene surface temperature is shown in Figure 10i. When the input power is 0 W, the surface temperature of the graphene is the same as that of room temperature. With the increase of input power, the surface temperature is gradually improved. The average surface temperature T of graphene can be expressed as [7]:
Infrared thermal images and average surface temperature of graphene (sample 2) with different amplitude of input power [10] (a) no power is applied; (b) input power q0 is 0.0007 W; (c) q0 is 0.01 W; (d) q0 is 0.03 W; (e) q0 is 0.05 W; (f) q0 is 0.08 W; (g) q0 is 0.11 W; (h) q0 is 0.16 W. (i) The average surface temperature of graphene versus the applied electric power. The experimental and theoretical results are shown. The SP is recorded at 16 kHz sound frequency and 5 cm measurement distance.
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where ks and Cp,s are the thermal conductivity and heat capacity of the paper substrate, respectively; ω is the angular frequency of sound. The average temperature is linearly related to the input power, and the theoretical curve is in good agreement with the experimental results. Combined with the test results of Figure 8c, the SP increases with the input power, which indicates that the Joule heating is related to the SP and the working mechanism is thermoacoustic effect.
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4. Graphene earphones: entertainment for both humans and animals
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Laser scribing can be used to prepare graphene due to its advantage of low cost, high speed and no transfer. The earphone based on laser scribed graphene (LSG) can cover audible range and ultrasonic range. Animal hearing is more sensitive to the sound of the ultrasound band than audible domain. Therefore, the graphene earphones can be applied not only to humans, but also to animals. This section will introduce laser direct writing to prepare graphene earphones. The advantage of fabricating graphene earphones by using the laser scribing technology is that the large scale array can be prepared without the mask. Figure 11a shows the preparation process of graphene earphones. The graphene oxide (GO) solution can be directly coated on PET substrates. Then the GO film can be reduced to graphene under 788 nm laser irradiation by using the DVD light engraving machine. It is noted that the preparation of the wafer-scale precise graphene earphones requires only 25 minutes by using laser scribing technology. Figure 11b shows the wafer-scale flexible graphene earphones on the PET substrate. The inset in Figure 11b shows the graphene earphones at 1 cm2 dimensions. The SEM image of graphene sheets is shown in Figure 11c. Before the laser scribing, the GO film is quite flat and dense. After the laser scribing, a graphene film of 10 μm thickness is obtained. It is noticed that there is an almost 10-fold thickness increase for the LSG compared to the original GO film. Figure 11d shows the I-V curves of the film before and after the laser scribing. The resistance of the GO film is 580 MΩ. However, when the GO film is reduced to the LSG, the small resistance can be obtained as 8.2 kΩ, which is an almost five orders of magnitude reduction.
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Figure 11.
Laser scribing technology for flexible graphene earphone fabrication [13]. (a) Process flow of the graphene earphone fabrication; (b) wafer-scale flexible graphene earphones. The inset shows an optical microscope image of the LSG earphone; (c) SEM image of the LSG and GO in false color; (d) electrical properties before and after laser scribing.
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Graphene sound source devices can be packaged into traditional earphones. After the laser scribing of the wafer-scale graphene patterns, these patterns could be cut into individual graphene earphones. Figure 12a shows the graphene earphone with an area of 10 × 10 mm2. Silver paste is applied on both sides of the graphene film to establish electrical input. The graphene earphones are finally connected to the electrical wiring of a commercial earphone casing using copper wires, as shown in Figure 12b. The structure of the graphene earphone is made up of the top cap, the graphene sheets, the Ag electrodes, the PET, and the bottom cap, as shown in Figure 12c. The packaged graphene earphones for human use is shown in Figure 12d. Compared with the traditional earphones, the distinctive feature of graphene device is significantly thinner than a voice coil. In order to obtain a sufficiently high SPL and equal-frequency playback sound, the periphery circuit is successfully designed. The schematic diagram of the circuit is shown in Figure 13. It is worth mentioning that the input sound frequency is doubled due to the thermoacoustic effect, and this needs to be compensated during actual testing, as described ahead. The drive circuit uses a USB port to apply power to the circuit for amplifying the AC signal and also to apply an up to 15 V DC bias to the graphene earphone. In this way, the graphene earphone can connect to a laptop for playing music.
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Figure 12.
The demonstration of graphene earphone [13]. (a) Graphene earphone in hand. (b) View of the graphene earphone in a commercial earphone casing. (c) Exploded view of a packaged graphene earphone. (d) A pair of graphene earphones in its final packaged form.
Figure 14a shows the graphene earphones acoustic test platform. Figure 14b shows the acoustic spectrum of the device. The graphene earphone is placed 1 cm away from the standard microphone. It can be seen that the graphene earphone has a flatter acoustic spectrum output than commercial earphone. Meanwhile, the sound intensity of the graphene earphone remains relatively stable. The graphene earphone has a fluctuation of 10 dB, which is much lower than the normal commercial earphone with the fluctuation of 30 dB. Especially, the spectrum can not only cover the 20 Hz to 20 kHz, but also cover the ultrasonic frequency band of 20–50 kHz. Animals are more sensitive to ultrasonic frequency than audible range, and graphene earphone has flat acoustic output in ultrasonic band. Therefore, graphene earphones can be used to train and control animal behaviors.
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Figure 14.
Sound pressure and frequency characteristics of the graphene earphone [13]. (a) Experimental setup for the graphene earphone. (b) Sound pressure level (SPL) curves of a graphene earphone compared with a commercial earphone.
\n
Finally, an application of the graphene earphone is demonstrated. The graphene earphone is fixed on the steel ring so that a dog can wear it, as shown in Figure 15a and b. The response test process is divided into the following sections. First, the dog needs to be trained so that it can recognize the “stand up” command when hearing the 35 kHz sound wave. The Chinese audio frequency of “stand up” was recorded first. Then, the audio was mixed with 35 kHz sine wave by computer software. The sampling frequency was 88.2 kHz, which was more than twice that of 35 kHz to ensure that the sampling was not distorted. The pure human voice was played to the dog, and the dog will stand up, and then it will be mixed with 35 kHz sound waves. The dog will be given a food reward every time he stands up successfully. And then it went on to increase the proportion of 35 kHz sound waves, and to reduce the proportion of human voice. Until the final 35 kHz sound waves was 100%, the dog was able to stand up to prove that it was successful in establishing a 35 kHz sound wave to the dog and the “stand up” command. Figure 15c shows a demonstration of controlling dog behavior through graphene earphones. The initial state of the dog is sitting, and when the dog hears the 35 kHz sound from the graphene earphone, it will stand up, which indicates that the graphene earphone can control the dog’s behavior.
\n
Figure 15.
Animal responding to ultrasound signals through graphene earphone [13]. (a) Graphene earphone for dogs. (b) Subject wearing the graphene earphone. (c) Wearing a graphene earphone for the dog to establish a 35 kHz sound wave and a “stand up” command to reflect the training process. The dog is initially sitting down. After receiving a familiar 35 kHz signal, it stands up.
\n
\n
\n
5. An intelligent artificial throat with sound-sensing ability based on laser-induced graphene
\n
The graphene sound source device based on thermoacoustic effect is introduced. Based on its piezoresistive effect, graphene can also be used for throat detection, which can be used as a good sound receiver. Therefore, based on thermoacoustic effect and piezoresistive effect, graphene is expected to realize transceiver integration.
\n
At the present stage, devices with integrated sound and transceiver function are usually called ultrasonic transducers, which can only work in the ultrasonic frequency band or under water [14]. At the same time, these substrates are hard, not flexible and biocompatible, and not suitable for wearable applications. Therefore, studying the audible frequency band, the sound-flexible device with good flexibility and biocompatibility is of great significance.
\n
Graphene force acoustic devices are mainly faced with two problems: (1) Sound receiving device based on graphene piezoresistive effect cannot be used as sound emitting device because these devices are wrapped in a polymer and the heat cannot be released into the air [15]. Or the device has a higher resistance, resulting in poor thermoacoustic effects [16]. (2) Sounding devices based on the thermoacoustic effect of graphene are also not suitable as sound receivers. Because these device are fabricated by using single layer or few layer graphene [17]. These kinds of graphenes are easily broken, which cause the devices damage. Graphene devices based on conventional processes and methods are difficult to simultaneously receive sound and emit sound using thermoacoustic effects and piezoresistive effects. Therefore, it is necessary to explore the realization of graphene transceiver sound integration based on new materials and new technology.
\n
The method of laser scribed graphene (LSG) is described above. However, graphene reduced by this method cannot be simultaneously transmitted and received sound due to packaging reasons. In 2014, Lin et al. proposed a method for preparing porous structure graphene by laser reduction of polyamide material (PI) [18]. This method can be completed in a single step process without the need to drop coating GO, and the prepared graphene has a porous structure and a good piezoresistive effect. Because graphene materials have good thermal properties, they can make sound based on thermoacoustic effect. Therefore, the porous graphene based on laser reduction can be used to fabricate the flexible force acoustic device to realize sound transmission and reception integration.
\n
This chapter introduces the process of preparing porous graphene by 450 nm wavelength laser. The porous graphene has the ability to integrate sound transmission and reception based on thermoacoustic effect and piezoresistive effect. Hence, a new intelligent artificial throat was prepared based on porous graphene. This device can be attached to the throat to sense the vibration mode of the throat of the deaf mute, and emit a preset sound when a specific throat vibration mode is detected. Therefore, the artificial throat can assist the deaf and mute to achieve sound, which will have potential application in biomedical, acoustic and other fields.
\n
Laser direct writing technology promotes the fast growth of porous, and a low-cost and portable laser platform is chosen. Figure 16a is a schematic diagram of a laser processing platform. The 450 nm laser can directly reduce the yellow PI to the porous graphene. Then, the artificial throat based on the porous graphene has been integrated to achieve the functions of emitting and detecting sounds, as shown in Figure 16b. Figure 16c shows the working mechanism of the artificial throat. When AC voltage is applied to the device, the periodic joule heat will cause air to expand, thus producing sound waves. When a low bias voltage is applied to the device, the vibration of the throat leads to the change of the resistance of the device, resulting in current fluctuations. Therefore, this device can act as both sound sources and detectors. A coughs, buzzing or screams can cause throats to vibrate, which can be detected by LSG artificial throats, and then LSG artificial throats can produce controllable sounds. Therefore, LSG artificial throat can achieve the conversion from meaningless sound to controllable and pre-designed sound. Figure 16d shows the image of LSG at laser power ranging from 20 to 350 mW. It can be seen that there is no obvious LSG at the bottom when the power is 20 mW. The second black part and the fourth black part from the bottom, which are produced at the power of 125 and 290 mW, respectively, are chosen to show the SEM image, as shown in Figure 16e–j. It can be seen that regular ridge lines are formed from top to bottom along the laser scanning trajectory. The line width is approximately 100 μm, which is similar to the focused spot size of the laser. As the laser power increases, the morphological differences are significant. A polygonal porous carbon film appears at the power of 125 mW. However, when the power is 290 mW, more porous irregular structures can be produced. This is mainly because the high power will lead to a sharp rise of the PI local temperature, thus breaking the C–O, C═O and N–C bonds, and causing the venting of some carbonaceous and nitric gases. Therefore, the porous structure of sample is formed due to the production and discharge of gases.
\n
Figure 16.
(a) One-step fabrication process of LSG. (b) LSG has the ability of emitting and detecting sound in one device. (c) The artificial throat can detect the movement of throat and generate controllable sound, respectively. (d) Six LSG samples produced by 450 nm laser with different power ranging from 20 to 350 mW. (e) The morphology of LSG sample produced at 290 mW under scanning electron microscopy, scale bar, 150 μm. (f) The morphology of LSG sample produced at 290 mW under high magnification, scale bar, 5 μm. (g) Cross-sectional view of LSG sample produced at 290 mW, scale bar, 12.5 μm. (h) The morphology of LSG sample produced at 125 mW under scanning electron microscopy, Scale bar, 150 μm. (i) The morphology of LSG sample produced at 125 mW under high magnification, Scale bar, 5 μm. (j) Cross-sectional view of LSG sample produced at 125 mW, scale bar, 12.5 μm [19].
\n
Four samples of LSG artificial throats are produced at different laser powers. The area of the LSG is around 1 × 2 cm2. The laser powers are 125, 200, 290 and 350 mW, respectively. The average thickness of LSG is about 8, 22, 38 and 60 μm, corresponding to the laser power. Figure 17a is a test diagram of the device. The distance between the sample and standard microphone is 2.5 cm. Figure 17b shows the relationship of the LSG (produced under 125 mW) between input power and SP. The result indicates that the SP increases linearly with increasing input power, and a higher SP can be obtained at 20 kHz. Figure 17c shows the spectrum response of the LSG produced by different power. The input power is normalized to 1 W. It can be observed that the SPL gradually decreases with the increase of power. Figure 17d shows the comparison of theoretical curve and the experimental results. The experimental analysis matches well with the theory model. Figure 17e shows the experimental data and theoretical curve as a function of the thickness of the LSG under the frequency of 10 and 20 kHz. The SP is inversely proportional to the thickness of the sounding unit and the theoretical curve matches well with the experimental results. Figure 17f shows the stability of LSG. It can be seen that there are no signs of degradation or changes of SPL in the device performance in 3 hours.
\n
Figure 17.
(a) The LSG is clamped under a commercial microphone to test the performance of emitting sound, scale bar, 1 cm. (b) The plot of the SP versus the input power at 10 and 20 kHz. (c) The output SPL versus the frequency of LSG generated by the laser with different power. (d) The SPL versus the frequency showing that the model agrees well with experimental results. (e) The plot of the SP versus the thickness of LSG at 10 and 20 kHz. (f) The stability of output SPL over time [19].
\n
Except for emitting sound, the LSG artificial throat also has excellent responses when detecting sound. The 25 μm-thick PI can be chosen to produce the LSG due to obvious resistance change. The sample is fixed and the loudspeaker is placed 3 cm away from the artificial throat. The six kinds of audios including firecracker, cow, piano, helicopter, bird and drum are performed. Figure 18 shows the resistance response of the device at six kinds of audios. Although the sampling frequency of this artificial throat is 100 Hz, which is far lower than the frequency of sound, it can be still noticed that the responses of the transducer are well synchronous to these original audio signals. Especially, the characteristic peaks are retained and reflected with high fidelity. Besides, the volume of loudspeaker has a great effect on the amplitude of the signal.
\n
Figure 18.
Responses towards different audios from a loudspeaker [19]. The LSG is placed 3 cm away from the loudspeaker. The orange insets above indicate the sound wave profiles of the original audios. Relative resistance changes show almost synchronous response to profiles of the original audios when the loudspeaker plays the audio of (a) firecrackers, (b) a cow, (c) a piano, (d) a helicopter, (e) a bird and (f) a drum.
\n
After identifying some kinds of audio clearly, LSG artificial throat is used to detect the vibration of throat cords. As shown in Figure 19a, the tester performs coughing, snoring and screaming twice in succession, and then the tester swallows and nods twice. After two consecutive tests, the test results are reproducible. In addition, swallowing and nodding can also cause muscle movement, which can also lead to changes in resistance. Fortunately, the waveforms of these muscle movements also have identifiable features. Different movement has its unique characteristic waveform as shown in Figure 19a, thus, the useful waveforms can be gotten by relying on the pattern recognition and machine learning. Interference from other activities can be identified and eliminated by multiple trainings in advance. Then, the tester makes the hums with four different tones as shown in Figure 19b, it can be seen that different tones have different responses, increasing the diversity of dumb people’s “language”. Especially, the hum tone 2 is same with the hum in Figure 19a. Furthermore, as shown in Figure 19c, the resistance increases as the sound intensity increases, which is due to the increase in mechanical vibration of the throat.
\n
Figure 19.
Responses towards different kinds of throat vibrations. (a) The LSG’s resistance changes towards the throat vibrations of the tester who makes two successive coughs, hums, screams, swallowing and nods. (b) The LSG’s resistance change caused by four different kinds of hum tones and the hum tone 2 is same with the hum in (a). (c) The relative resistance change of LSG increases with the increase of the sound intensities of the hum [19].
\n
\n
\n
6. Conclusion
\n
Thermoacoustic device for the current low reliability, high performance and poor driving voltage problem, Ren’s group successfully proposed and implemented graphene thermoacoustic devices with high performance, high reliability and low driving voltage. Besides, these devices have the advantages of low driving voltage, soft, transparent, thin thickness and wide band sound output, especially the extremely flat sound output in the ultrasonic frequency band. Exploring the application of graphene in the field of acoustics is the first time in the world and obtains the following three research results:
A multilayer graphene sound source device is proposed and realized. The sound output performance from 1 to 50 kHz is obtained. It is observed that there is a flat sound spectrum in the range of 20–50 kHz. The performance of multilayer graphene sound source device with different thickness is compared. It was found that graphene with thinner thickness had a higher SPL value.
A low cost graphene earphones at wafer level are realized by laser scribing. Graphene earphone has a wider and flatter acoustic spectrum output than commercial earphones. In addition, graphene earphone achieves control of animal behavior.
A wearable artificial throat that is manufactured in one step based on LSG has been implemented. The LSG device achieves the functional integration of emitting and detecting sound due to its excellent thermoacoustic and piezoresistive properties. The LSG artificial throat has a relatively broad frequency spectrum because of resonance-free oscillations of the sound sources. Besides, as a sound detector, the LSG artificial throat can capture the mechanical vibration of throat cords with a fine repetition.
\n
\n
Acknowledgments
\n
This work was supported by the National Key R&D Program (2016YFA0200400), National Natural Science Foundation (61434001, 61574083, 61874065, 51861145202), and National Basic Research Program (2015CB352101) of China. He Tian thanks for the support from Young Elite Scientists Sponsorship Program by CAST (2018QNRC001). The authors are also thankful for the support of the Research Fund from Beijing Innovation Center for Future Chip, Beijing Natural Science Foundation (4184091), and Shenzhen Science and Technology Program (JCYJ20150831192224146).
\n
\n',keywords:"graphene sound, acoustic, thermoacoustic effect, wide frequency range, flexible, large-scale",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/65428.pdf",chapterXML:"https://mts.intechopen.com/source/xml/65428.xml",downloadPdfUrl:"/chapter/pdf-download/65428",previewPdfUrl:"/chapter/pdf-preview/65428",totalDownloads:557,totalViews:0,totalCrossrefCites:0,dateSubmitted:"May 21st 2018",dateReviewed:"September 20th 2018",datePrePublished:"January 30th 2019",datePublished:"November 27th 2019",dateFinished:"January 30th 2019",readingETA:"0",abstract:"In 2011, Ren’s group has developed the first graphene sound source device in the world. This is the first time that the graphene applications have been extended into acoustic area. The graphene sound source can produce sound in a wide sound frequency range from 100 Hz to 50 kHz. After that, we have innovated the first graphene earphone, which can be used both for human and animals. In 2017, both the sound detection and sound emission have been integrated into one graphene device, which is called graphene artificial throat. In this book chapter, more details for developing those graphene acoustic devices will be introduced, which can help to boost the real applications of graphene devices.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/65428",risUrl:"/chapter/ris/65428",signatures:"He Tian, Guang-Yang Gou, Fan Wu, Lu-Qi Tao, Yi Yang and Tian-Ling Ren",book:{id:"6845",title:"Graphene and Its Derivatives",subtitle:"Synthesis and Applications",fullTitle:"Graphene and Its Derivatives - Synthesis and Applications",slug:"graphene-and-its-derivatives-synthesis-and-applications",publishedDate:"November 27th 2019",bookSignature:"Ishaq Ahmad and Fabian I. Ezema",coverURL:"https://cdn.intechopen.com/books/images_new/6845.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-1-83962-882-5",printIsbn:"978-1-83962-881-8",pdfIsbn:"978-1-83962-883-2",editors:[{id:"25524",title:"Prof.",name:"Ishaq",middleName:null,surname:"Ahmad",slug:"ishaq-ahmad",fullName:"Ishaq Ahmad"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Establishment of theoretical model",level:"1"},{id:"sec_3",title:"3. Graphene-on-paper sound source devices",level:"1"},{id:"sec_4",title:"4. Graphene earphones: entertainment for both humans and animals",level:"1"},{id:"sec_5",title:"5. An intelligent artificial throat with sound-sensing ability based on laser-induced graphene",level:"1"},{id:"sec_6",title:"6. Conclusion",level:"1"},{id:"sec_7",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'Novoselov KS, Geim AK, Morozov SV, et al. Electric field effect in atomically thin carbon films. Science. 2004;306:666-669'},{id:"B2",body:'Ho KI, Boutchich M, Su CY, et al. A self-aligned high-mobility graphene transistor: Decoupling the channel with fluorographene to reduce scattering. Advanced Materials. 2015;27:6519-6525'},{id:"B3",body:'Li D, Chen M, Zong Q, et al. Floating-gate manipulated graphene-black phosphorus heterojunction for nonvolatile ambipolar schottky junction memories, memory inverter circuits, and logic rectifiers. Nano Letters. 2017;17:6353-6359'},{id:"B4",body:'Sarker BK, Cazalas E, Chung TF, et al. Position-dependent and millimetre-range photodetection in phototransistors with micrometre-scale graphene on SiC. Nature Nanotechnology. 2017;12:668-674'},{id:"B5",body:'SMM Z, Holt M, Sadeghi MM, et al. 3D integrated monolayer graphene-Si CMOS RF gas sensor platform. npj 2D Materials and Applications. 2017;1:36'},{id:"B6",body:'Arnold HD, Crandall IB. The thermophone as a precision source of sound. Physical Review. 1917;10:22'},{id:"B7",body:'Shinoda H, Nakajima T, Ueno K, et al. Thermally induced ultrasonic emission from porous silicon. Nature. 1999;400:853-855'},{id:"B8",body:'Xiao L, Chen Z, Feng C, et al. Flexible, stretchable, transparent carbon nanotube thin film loudspeakers. Nano Letters. 2008;8:4539-4545'},{id:"B9",body:'Tian H. Graphene-Based Novel Micro/Nano Devices. Beijing: Tsinghua University; 2015'},{id:"B10",body:'Tian H, Ren TL, Xie D, et al. Graphene-on-paper sound source devices. ACS Nano. 2011;5:4878-4885'},{id:"B11",body:'Blackstock DT. Fundamentals of Physical Acoustics. Vol. 465. New York: John Wiley and Sons, Ltd; 2000. p. 440'},{id:"B12",body:'Vesterinen V, Niskanen AO, Hassel J, Helisto P. Fundamental efficiency of nanothermophones: Modeling and experiments. Nano Letters. 2010;10:5020-5024'},{id:"B13",body:'Tian H, Li C, Mohammad MA, et al. Graphene earphones: Entertainment for both humans and animals. ACS Nano. 2014;8:5883-5890'},{id:"B14",body:'Meeks SW, Timme RW. Rare earth iron magnetostrictive underwater sound transducer. The Journal of the Acoustical Society of America. 1977;62:1158-1164'},{id:"B15",body:'Park JJ, Hyun WJ, Mun SC, et al. Highly stretchable and wearable graphene strain sensors with controllable sensitivity for human motion monitoring. ACS Applied Materials & Interfaces. 2015;7:6317-6324'},{id:"B16",body:'Cheng Y, Wang R, Sun J, et al. A stretchable and highly sensitive graphene-based fiber for sensing tensile strain, bending, and torsion. Advanced Materials. 2015;27:7365-7371'},{id:"B17",body:'Tian H, Xie D, Yang Y, et al. Single-layer graphene sound-emitting devices: Experiments and modeling. Nanoscale. 2012;4:2272-2277'},{id:"B18",body:'Lin J, Peng Z, Liu Y, et al. Laser-induced porous graphene films from commercial polymers. Nature Communications. 2014;5:5714'},{id:"B19",body:'Tao LQ, Tian H, Liu Y, et al. An intelligent artificial throat with sound-sensing ability based on laser induced graphene. Nature Communications. 2017;8:14579'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"He Tian",address:"tianhe88@tsinghua.edu.cn; yiyang@tsinghua.edu.cn\nand rentl@tsinghua.edu.cn;",affiliation:'
Institute of Microelectronics, Tsinghua University, China
Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, China
Institute of Microelectronics, Tsinghua University, China
Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, China
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The company was founded in Vienna in 2004 by Alex Lazinica and Vedran Kordic, two PhD students researching robotics. While completing our PhDs, we found it difficult to access the research we needed. So, we decided to create a new Open Access publisher. A better one, where researchers like us could find the information they needed easily. The result is IntechOpen, an Open Access publisher that puts the academic needs of the researchers before the business interests of publishers.
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We started by publishing journals and books from the fields of science we were most familiar with - AI, robotics, manufacturing and operations research. Through our growing network of institutions and authors, we soon expanded into related fields like environmental engineering, nanotechnology, computer science, renewable energy and electrical engineering, Today, we are the world’s largest Open Access publisher of scientific research, with over 4,200 books and 54,000 scientific works including peer-reviewed content from more than 116,000 scientists spanning 161 countries. Our authors range from globally-renowned Nobel Prize winners to up-and-coming researchers at the cutting edge of scientific discovery.
\\n\\n
In the same year that IntechOpen was founded, we launched what was at the time the first ever Open Access, peer-reviewed journal in its field: the International Journal of Advanced Robotic Systems (IJARS).
\\n\\n
The IntechOpen timeline
\\n\\n
2004
\\n\\n
\\n\\t
Intech Open is founded in Vienna, Austria, by Alex Lazinica and Vedran Kordic, two PhD students, and their first Open Access journals and books are published.
\\n\\t
Alex and Vedran launch the first Open Access, peer-reviewed robotics journal and IntechOpen’s flagship publication, the International Journal of Advanced Robotic Systems (IJARS).
\\n
\\n\\n
2005
\\n\\n
\\n\\t
IntechOpen publishes its first Open Access book: Cutting Edge Robotics.
\\n
\\n\\n
2006
\\n\\n
\\n\\t
IntechOpen publishes a special issue of IJARS, featuring contributions from NASA scientists regarding the Mars Exploration Rover missions.
\\n
\\n\\n
2008
\\n\\n
\\n\\t
Downloads milestone: 200,000 downloads reached
\\n
\\n\\n
2009
\\n\\n
\\n\\t
Publishing milestone: the first 100 Open Access STM books are published
\\n
\\n\\n
2010
\\n\\n
\\n\\t
Downloads milestone: one million downloads reached
\\n\\t
IntechOpen expands its book publishing into a new field: medicine.
\\n
\\n\\n
2011
\\n\\n
\\n\\t
Publishing milestone: More than five million downloads reached
\\n\\t
IntechOpen publishes 1996 Nobel Prize in Chemistry winner Harold W. Kroto’s “Strategies to Successfully Cross-Link Carbon Nanotubes”. Find it here.
\\n\\t
IntechOpen and TBI collaborate on a project to explore the changing needs of researchers and the evolving ways that they discover, publish and exchange information. The result is the survey “Author Attitudes Towards Open Access Publishing: A Market Research Program”.
\\n\\t
IntechOpen hosts SHOW - Share Open Access Worldwide; a series of lectures, debates, round-tables and events to bring people together in discussion of open source principles, intellectual property, content licensing innovations, remixed and shared culture and free knowledge.
\\n
\\n\\n
2012
\\n\\n
\\n\\t
Publishing milestone: 10 million downloads reached
\\n\\t
IntechOpen holds Interact2012, a free series of workshops held by figureheads of the scientific community including Professor Hiroshi Ishiguro, director of the Intelligent Robotics Laboratory, who took the audience through some of the most impressive human-robot interactions observed in his lab.
\\n
\\n\\n
2013
\\n\\n
\\n\\t
IntechOpen joins the Committee on Publication Ethics (COPE) as part of a commitment to guaranteeing the highest standards of publishing.
\\n
\\n\\n
2014
\\n\\n
\\n\\t
IntechOpen turns 10, with more than 30 million downloads to date.
\\n\\t
IntechOpen appoints its first Regional Representatives - members of the team situated around the world dedicated to increasing the visibility of our authors’ published work within their local scientific communities.
\\n
\\n\\n
2015
\\n\\n
\\n\\t
Downloads milestone: More than 70 million downloads reached, more than doubling since the previous year.
\\n\\t
Publishing milestone: IntechOpen publishes its 2,500th book and 40,000th Open Access chapter, reaching 20,000 citations in Thomson Reuters ISI Web of Science.
\\n\\t
40 IntechOpen authors are included in the top one per cent of the world’s most-cited researchers.
\\n\\t
Thomson Reuters’ ISI Web of Science Book Citation Index begins indexing IntechOpen’s books in its database.
\\n
\\n\\n
2016
\\n\\n
\\n\\t
IntechOpen is identified as a world leader in Simba Information’s Open Access Book Publishing 2016-2020 report and forecast. IntechOpen came in as the world’s largest Open Access book publisher by title count.
\\n
\\n\\n
2017
\\n\\n
\\n\\t
Downloads milestone: IntechOpen reaches more than 100 million downloads
\\n\\t
Publishing milestone: IntechOpen publishes its 3,000th Open Access book, making it the largest Open Access book collection in the world
We started by publishing journals and books from the fields of science we were most familiar with - AI, robotics, manufacturing and operations research. Through our growing network of institutions and authors, we soon expanded into related fields like environmental engineering, nanotechnology, computer science, renewable energy and electrical engineering, Today, we are the world’s largest Open Access publisher of scientific research, with over 4,200 books and 54,000 scientific works including peer-reviewed content from more than 116,000 scientists spanning 161 countries. Our authors range from globally-renowned Nobel Prize winners to up-and-coming researchers at the cutting edge of scientific discovery.
\n\n
In the same year that IntechOpen was founded, we launched what was at the time the first ever Open Access, peer-reviewed journal in its field: the International Journal of Advanced Robotic Systems (IJARS).
\n\n
The IntechOpen timeline
\n\n
2004
\n\n
\n\t
Intech Open is founded in Vienna, Austria, by Alex Lazinica and Vedran Kordic, two PhD students, and their first Open Access journals and books are published.
\n\t
Alex and Vedran launch the first Open Access, peer-reviewed robotics journal and IntechOpen’s flagship publication, the International Journal of Advanced Robotic Systems (IJARS).
\n
\n\n
2005
\n\n
\n\t
IntechOpen publishes its first Open Access book: Cutting Edge Robotics.
\n
\n\n
2006
\n\n
\n\t
IntechOpen publishes a special issue of IJARS, featuring contributions from NASA scientists regarding the Mars Exploration Rover missions.
\n
\n\n
2008
\n\n
\n\t
Downloads milestone: 200,000 downloads reached
\n
\n\n
2009
\n\n
\n\t
Publishing milestone: the first 100 Open Access STM books are published
\n
\n\n
2010
\n\n
\n\t
Downloads milestone: one million downloads reached
\n\t
IntechOpen expands its book publishing into a new field: medicine.
\n
\n\n
2011
\n\n
\n\t
Publishing milestone: More than five million downloads reached
\n\t
IntechOpen publishes 1996 Nobel Prize in Chemistry winner Harold W. Kroto’s “Strategies to Successfully Cross-Link Carbon Nanotubes”. Find it here.
\n\t
IntechOpen and TBI collaborate on a project to explore the changing needs of researchers and the evolving ways that they discover, publish and exchange information. The result is the survey “Author Attitudes Towards Open Access Publishing: A Market Research Program”.
\n\t
IntechOpen hosts SHOW - Share Open Access Worldwide; a series of lectures, debates, round-tables and events to bring people together in discussion of open source principles, intellectual property, content licensing innovations, remixed and shared culture and free knowledge.
\n
\n\n
2012
\n\n
\n\t
Publishing milestone: 10 million downloads reached
\n\t
IntechOpen holds Interact2012, a free series of workshops held by figureheads of the scientific community including Professor Hiroshi Ishiguro, director of the Intelligent Robotics Laboratory, who took the audience through some of the most impressive human-robot interactions observed in his lab.
\n
\n\n
2013
\n\n
\n\t
IntechOpen joins the Committee on Publication Ethics (COPE) as part of a commitment to guaranteeing the highest standards of publishing.
\n
\n\n
2014
\n\n
\n\t
IntechOpen turns 10, with more than 30 million downloads to date.
\n\t
IntechOpen appoints its first Regional Representatives - members of the team situated around the world dedicated to increasing the visibility of our authors’ published work within their local scientific communities.
\n
\n\n
2015
\n\n
\n\t
Downloads milestone: More than 70 million downloads reached, more than doubling since the previous year.
\n\t
Publishing milestone: IntechOpen publishes its 2,500th book and 40,000th Open Access chapter, reaching 20,000 citations in Thomson Reuters ISI Web of Science.
\n\t
40 IntechOpen authors are included in the top one per cent of the world’s most-cited researchers.
\n\t
Thomson Reuters’ ISI Web of Science Book Citation Index begins indexing IntechOpen’s books in its database.
\n
\n\n
2016
\n\n
\n\t
IntechOpen is identified as a world leader in Simba Information’s Open Access Book Publishing 2016-2020 report and forecast. IntechOpen came in as the world’s largest Open Access book publisher by title count.
\n
\n\n
2017
\n\n
\n\t
Downloads milestone: IntechOpen reaches more than 100 million downloads
\n\t
Publishing milestone: IntechOpen publishes its 3,000th Open Access book, making it the largest Open Access book collection in the world
\n
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