Open access peer-reviewed chapter

Low-Frequency Coherent Raman Spectroscopy Using Spectral-Focusing of Chirped Laser Pulses

By Masahiko Tani, Masakazu Hibi, Kohji Yamamoto, Mariko Yamaguchi, Elmer S. Estacio, Christopher T. Que and Masanori Hangyo

Submitted: April 13th 2011Reviewed: October 18th 2011Published: February 24th 2012

DOI: 10.5772/32734

Downloaded: 2397

1. Introduction

Centrifugal pumps are some of the most widely used pumps in the industry (Bachus & Custodio, 2003) and many of them are driven by induction motors. Failure to either the induction motor or the centrifugal pump would result in an unscheduled shutdown leading to loss of production and subsequently loss of revenue. A lot of effort has been invested in detecting and diagnosing incipient faults in induction motors and centrifugal pumps through the analysis of vibration data, obtained using accelerometers installed in various locations on the motor-pump system. Fault detection schemes based on the analysis of process data, such as pressures, flow rates and temperatures have also been developed. In some cases, speed is used as an indicator for the degradation of the pump performance. All of the above mentioned schemes require sensors to be installed on the system that leads to an increase in overall system cost. Additional sensors need cabling, which also contributes towards increasing the system cost. These sensors have lower reliability, and hence fail more often than the system being monitored, thereby reducing the overall robustness of the system. In some cases it maybe difficult to access the pump to install sensors. One such example is the case of submersible pumps wherein it is difficult to install or maintain sensors once the pump is underwater. To avoid the above-mentioned problems, the use of mechanical and/or process sensors has to be avoided to the extent possible.

Motor current signature analysis (MCSA) and electrical signal analysis (ESA) have been in use for some time (Benbouzid, 1998, Thomson, 1999) to estimate the condition of induction motors based on spectral analysis of the motor current and voltage waveforms. The use of motor electrical signals to diagnose centrifugal pump faults has started to gain prominence in the recent years. However, it would be more beneficial if the drive power system (motor-pump system) as a whole is monitored. The large costs associated with the resulting idle equipment and personnel due to a failure in either the motor or the pump can often be avoided if the degradation is detected in its early stages (McInroy & Legowski, 2001). Moreover, the downtime can be further reduced if the faulty component within the drive power system can be isolated thereby aiding the plant personnel to be better prepared with the spares and repair kits. Hence there is not only a strong need for cost-effective schemes to assess the “health" condition of the motor-pump system as a whole, but also a strong requirement for an efficient fault isolation algorithm to isolate the component within the motor-pump system that is faulty. The unique contribution of this work is that it uses only the motor electrical signals to detect and isolate faults in the motor and the pump. Moreover, it does not presume the existing “health” condition of either the motor or the pump and detects the degradation of the system from the current state.

2. Literature review

Most of the literature on fault detection of centrifugal pumps isbased on techniques that require the measurement of either vibrationor other process based signals. There are very few peer-reviewed publicationsthat deal with non-invasive/non-intrusive techniques todiagnose faults in centrifugal pumps. Even fewer literatures are available on the isolation of faults between the pump and the motor driving the pump. In this chapter, only the publications thatdeal with detecting centrifugal pump faults using motor electrical signals are reviewed.In (Dister, 2003), the authors review the latest techniques thatare used in pump diagnostics. Hardware and software algorithms required to make accurate assessment of the pump condition are also discussed. Lists of typical problems thatdevelop in the pump along with the conventional methods of detectionare presented. In (Siegler, 1994), the authors describe the development andapplication of signal processing routines for the detection oferoded impeller condition of a centrifugal pump found in submarines. Fault features are extracted from thepower spectrum and a neural networks-based classification schemebased on the nearest neighborhood technique classifies about 90%of the test cases correctly. In(Casada, 1994, 1995, 1996a) and (Casada & Bunch, 1996b), motor current and power analysis is usedto detect some operational and structural problems such as cloggedsuction strainer and equipment misalignment. Load related peaks fromthe power or current spectrum are used as fault indicators in theproposed scheme. A comparative study between the vibration spectrum-based, power spectrum-based and torque spectrum-based detection methods is also described in detail. The authors conclude that the motor-monitored parameters are much more sensitive than the vibration transducers in detecting effects of unsteady process conditions resulting from both system and process specific sources. In (Kenull et al., 1997), the energy content of themotor current signal in specific frequency ranges are used as faultindicators to detect faults that occur in centrifugalpumps, namely, partial flow operation, cavitation, reverse rotation,etc. The work in (Dalton & Patton, 1998) deals with the development of a multi-model fault diagnosis system of an industrial pumping system. Two different approaches to model-based fault detection are outlined based on observers and parameter estimation. In (Perovic, Unsworth & Higham, 2001), fault signatures are extracted from themotor current spectrum by relating the spectral features to theindividual faults to detect cavitation, blockage and damagedimpeller condition. A fuzzy logic system is also developed toclassify the three faults. The authors conclude that the probability of fault detection varies from 50% to 93%. The authors also conclude that adjustments to the rules or the membership functions are required so that differences in the pump design and operating flow regimes can be taken into consideration. In (Schmalz & Schuchmann, 2004), the spectral energy within the band of about 5Hz to 25 Hz is calculated and is used to detect the presence ofcavitation or low flow condition in centrifugal pumps. In (Welch et al., 2005) and (Haynes et al., 2002), the electrical signal analysis isextended to condition monitoring of aircraft fuel pumps. The frontbearing wear of auxiliary pumps is selected to demonstrate theeffectiveness of the proposed algorithm. The authors after considerable study establish that the best indicator of front bearing wear in the motor current spectrum is not any specific frequency peak but is the base or floor of themotor current spectrum. The noisefloor of the current spectrum is observed to increase in all pumpshaving degraded front bearings. In (Kallesoe et al., 2006), a model-based approach using a combination of structural analysis, observer design and analytical redundancy relation (ARR) design is used to detect faults in centrifugal pumps driven by induction motors. Structural considerations are used to divide the system into two cascaded subsystems. The variables connecting the two subsystems are estimated using an adaptive observer and the fault detection is based on an ARR which is obtained using Groebner basis algorithm. The measurements used in the development of the fault detection method are the motor terminal voltages and currents and the pressure delivered by the pump. In (Harris et al., 2004), the authors describe a fault detection system for diagnosing potential pump system failures using fault features extracted from the motor current and the predetermined pump design parameters. In (Hernandez-Solis & Carlsson, 2010), the motor current and power signatures are analyzed to not only detect when cavitation in the pump is present, but also when it starts. The correlation between the pump cavitation phenomena and the motor power is studied at different pump operating conditions.

Most of the detection schemes presented in the above-mentioned literatureare based on either tracking the variation of the characteristicfault frequency or computing the change in the energy content of themotor current in certain specific frequency bands. Thecharacteristic fault frequency depends on the design parameters,which are not easily available. For example, the rolling elementbearing fault frequency depends on the ball diameter, pitch,contact angle, etc (McInerny & Dai, 2003). This information is not available, unless thepump is dismantled. Changes in the energy content within certainspecific frequency bands could also result due to changes in the power supplyor changes in the load even without any fault in the pump or these changes could also occur if a fault initiates in the induction motor that is driving the pump. Hence,this would result in the generation of frequent false alarms. Based on these discussions it can be seen that there is a strong need to develop a non-intrusive/non-invasive fault detection and isolation algorithm to detect and isolate faults in centrifugal pumps that is not only independentof the motor and pump design parameters but also independent of power supply and load variations.

3. Overview of fault detection methods

A fault detection system is said to perform effectively if itexhibits a high probability of fault detection and a low probabilityof false alarms. Fig. 1 shows the different characteristics any fault detection method exhibits. If the detection scheme is too sensitive then it islikely to generate frequent false alarms which lead to operatorsquestioning the effectiveness of the detection method. At the sametime if the detection scheme is too insensitive then there is achance of missing anomalies that might lead to a fault. Missed faults may lead to critical equipment failure leading to downtime.As a result a balance between the fault detectioncapability and the false alarm generation rate must be achieved when designinga fault detection scheme. The fault detection methods can be broadly classified into two broad categories, namely, signal-based fault detection methods and model-based fault detection methods. Fig. 2 compares the procedure of a signal-based and model-based fault detection method.

Figure 1.

Fault detection method characteristics

Figure 2.

a) Signal-based fault detection method; (b) Model-based fault detection method

3.1. Signal-based fault detection method

Signal-based fault detection techniques are based on processing and analyzing raw system measurements such as motor currents, vibration signals and/or other process-based signals. No explicit system model is used in these techniques. Fault features are extracted from the sampled signals and analyzed for the presence or lack of a fault. However, these system signals are impacted by changes in the operating conditions that are caused due to changes in the system inputs and/or disturbances. Hence, if one were to analyze only the system signals for the presence of a fault, then it would be difficult to distinguish the fault related features from the input and disturbance induced features. This would result in the generation of frequent false alarms, which would in turn result in the plant personnel losing confidence over the fault detection method. If the system is considered to be ideal, i.e., there are no changes in the input and a constant input is supplied to the system and there are no disturbances affecting the system, then the signal-based detection schemes can be used in the detection of system faults with 0% false alarms. However, in reality such a system does not exist. The input variations cannot be controlled and harmonics are injected into the system and into the system signals. Moreover, disturbances to the system always occur and are always never constant. Hence these variations affect the system signals and result in the generation of false alarms.

3.2. Model-based fault detection method

The basic principle of a model-based fault detection scheme is to generate residuals that are defined as the differences between the measured and the model predicted outputs. The system model could be a first principles-based physics model or an empirical model of the actual system being monitored. The model defines the relationship between the system outputs, system faults, system disturbances and system inputs. Ideally, the residuals that are generated are only affected by the system faults and are not affected by any changes in the operating conditions due to changes in the system inputs and/or disturbances. That is, the residuals are only sensitive to faults while being insensitive to system input or disturbance changes (Patton & Chen, 1992). If the system is “healthy”, then the residuals would be approximated by white noise. Any deviations of the residuals from the white noise behavior could be interpreted as a fault in the system.

In (Harihara et al., 2003), signal-based and model-based fault detection schemes are compared to a flip-of-a-coin detector as applied to induction motor fault detection. The results of the study can be extended to centrifugal pump detection also. Receiver operating characteristic (ROC) curves are plotted for all the three types of detection schemes and their performances are compared with respect to the probability of false alarms and probability of fault detection. For false alarm rates of less than 50%, the flip-of-a-coin fault detector outperformed the signal-based fault detection scheme for the cases under consideration. It was possible to achieve 100% fault detection capability using the signal-based detection method, but at the same time there was a very high probability of false alarms (about 50%). On the contrary, the model-based fault detection method operated with 0% false alarm rates and had approximately 89% fault detection capability. If the constraint on the false alarm probability was relaxed to about 10% then it was possible to achieve 100% fault detection capability using the model-based detection technique.

4. Proposed fault diagnosis method

The fault diagnosis algorithm can be broadly classified into a three-step process; namely, fault detection, fault isolation and fault identification. The proposed fault diagnosis method in this chapter addresses the first two steps of the diagnostic process. It combines elements from both the signal-based and model-based diagnostic approaches. An overall architecture of the proposed method is shown in Fig. 3.

Figure 3.

Overall architecture of the proposed fault diagnosis method

The data acquisition module samples the three-phase voltages and three-phase currents. The data preprocessing module consists of down-sampling, scaling and signal segmentation. The sampled signals are down-sampled to match the sampling rate of the developed system model and normalized with respect to the motor nameplate information. In general, the motor electrical measurements are non-stationary in nature. However, traditional signal processing techniques such as FFT can be used to analyze these signals if quasi-stationary regions within these signals are identified. If identified, then only these segments of the signals are analyzed for the presence of a fault. A signal segmentation algorithm developed in this research is applied to the scaled motor electrical signals to determine the quasi-stationary segments within the signals. For a signal to be considered quasi-stationary, its fundamental frequency component and the corresponding harmonic components must remain constant over time. Thus as part of the signal segmentation algorithm, the time variations of the spectral components of the sampled signals are investigated and only those time segments of the sampled signals during which the spectral components are constant are considered for further analysis. Moreover, only the spectral components with large magnitudes are considered as those with very small amplitudes do not contribute significantly to the overall characteristics of the signal. Since the resulting signals are quasi-stationary in nature, Fourier-based methods can be applied to extract the fault features.

4.1. Proposed fault detection method

The schematic of the proposed fault detection method is shown in Fig. 4. As mentioned in the previous section, the proposed method combines elements from both the signal-based and model-based fault detection methods. The quasi-stationary segments of the pre-processed signals are used as inputs to both the “system model” module and the “fault feature extraction” module. Residuals are generated between the fault indicators extracted from the system signals and the fault features estimated by the system model. These residuals are further analyzed to detect the presence of a fault in the system.

Figure 4.

Schematic of the proposed fault detection method

4.1.1. Description of the fault detection indicator

Most of the available literatures are based on extracting and tracking the variation of specificcharacteristic frequencies. There are certain limitations associated with thisapproach. One is the motor and/or pump design parametersor physical model parameters are required to obtain suchcharacteristic frequencies. Secondly, the motor current spectrum is usually contaminated by load variations resulting in false indications of fault presence, though load compensation can remedy this. To overcome these limitations, the proposed fault indicator is based on monitoring the harmonic content of the motor current signals. This is based on the premise that any change in the ``health" of the system would induce harmonic changes in the motor torque which would in turn induce harmonic changes in the motor current.

The Short Term Fourier Transforms (STFT) is used to process the motor current signals. In this study, the proposed fault indicator is defined as:

FDI(k)=13a,b,ckIk2If2E1
.

where a, b and c are the three phases of the motor current, I k is the RMS value of the kth harmonic component in the motor current and I f is the fundamental frequency component of the motor current.

4.1.2. Description of the system model

To reduce the generation of false alarms and maintain a good fault detection capability, the effects of the changing input conditions must be isolated. In this study, this is accomplished by means of an empirical model. The developed model describes the relation between the baseline (or “healthy”) response of the system and the system inputs. The baseline response of the system is described by the fault indicator of a “healthy” system. The inputs to the model are derived from the preprocessed system signals. They include energy content and harmonic distortion of the voltage signal, system load level etc. The model structure used in this study is of the form:

Γ[Λ(V(t)),Ψ(I(t)),FDI]=0E2

whereΓis the unknown function to be modeled,Λis the transformation function that converts the preprocessed voltage signals to the system model inputs,Ψis the transformation function that converts the preprocessed current signals to the system model inputs, V(t) is the time varying preprocessed voltage signals, I(t) is the time varying preprocessed current signals and FDI is the fault indicator described in the previous subsection. In this study, the unknown functionΓis modeled as a polynomial having the structure similar to a nonlinear ARX model. The accuracy of the model output depends on the nature (accuracy, volume, etc) of the raw data used in the training phase. Hence the system is operated in a sufficiently wide range to cover the entire operating envelope of interest. The proposed model is developed using data collected from the “healthy” baseline system. The developed model predicts the baseline fault indicator estimate for a given operating condition characterized by the model inputs. The model is validated using data that are different from the one used in its development.

Another important observation to note is that no fault data are usedto train the model. Hence for anomalies in the pump or motor, theoutput of the model will be the system baseline fault indicator estimate (or the “healthy” system FDI estimate) for the given operating condition. No motor or pump designparameters are used in the development of the baseline model. Hencethis model can be easily ported to other motor-centrifugal pumpsystems, as only the measured motor voltages and currents are used in model development. However, each motor-centrifugal pump systemwill have a different baseline model, which can be adaptivelydeveloped using the measured motor electrical signals.

4.1.3. Analysis of residuals and decision making

An average of the model estimated output (“healthy” system FDI estimate) is compared to theaverage of the FDI extracted from themeasured signals and the residuals between the two are computed. The computed residualis then normalized with respect to the average of the model estimatedoutput and is tracked over time. This normalized residual is defined as the faultdetection indicator change (FDIC). Let the size of the moving window within the timesegment [t1, tN] be (t2 – t1) and the moving distance of the window be p.The FDIC is computed as

FDIC=i=t1+kpt2+kpFDI(i)i=t1+kpt2+kpFD^I(i)i=t1+kpt2+kpFD^I(i),k=0,1,2,...mE3

where m = (tN - t2)/p. If the system is “healthy”, then the FDIC can be approximated by white noise. However, if there is a fault in the system, then the FDIC will deviate from the white noise behavior. If this deviation exceeds a certain threshold then a “fault” alarm is issued. Otherwise, the system is considered “healthy” and the procedure is repeated. If the detection threshold is chosen to be very large, then although the false alarm rates are reduced, there is a very high probability of missing a fault. Similarly, if the detection threshold is chosen to be very small then along with good fault detection capability, there is a very high probability of generating false alarms. Hence a balance has to be achieved in deciding the detection threshold. One factor in choosing the threshold is the intended application of the detection method or the system that is being monitored. For example, in space applications, a high rate of false alarms is acceptable as people’s lives are at stake. Hence the threshold can be chosen small to detect any anomaly. In utility industries however, false alarms are not tolerated and hence a somewhat higher threshold is preferred. The detection method might not detect the fault as soon as the fault initiates, but will detect it as the fault degrades and well before any catastrophic failure. In this study, an integer multiple of the standard deviation of the “healthy” baseline variation is used as the detection threshold.

4.2. Proposed fault isolation method

The output of the system model developed in the previous subsection is affected by either a fault in the induction motor or a fault in the centrifugal pump or any other component affecting the motor output. The reason is that the model is developed for the entire system (motor-pump) as a whole. For the purpose of this study only motor and pump faults are assumed. Hence, it is not possible to isolate a developing fault. To distinguish between faults in the motor and faults in the pump, a localized model of one of the components is required wherein the output of the model is affected only by the faults in that component and is insensitive to the faults in the other. In this study, since no measurement is available from the centrifugal pump, a localized model for the induction motor is developed. The output of this model is only sensitive to faults in the motor and is insensitive to faults in the centrifugal pump. The fault isolation method is used to distinguish between motor and pump faults only when a fault within the system is detected. If the system is “healthy”, then the next data set is analyzed to check for the presence or lack of fault and the fault isolation method is not used.

4.2.1. Development of the localized induction motor model

Consider an induction machine such that the stator windings are identical, sinusoidally distributed windings, displaced by 120 , with N s equivalent turns and resistance, r s . Consider the rotor windings as three identical sinusoidally distributed windings displaced by 120 , with N r equivalent turns and resistance, r r . The voltage equations are given as:

vabcs=rsiabcs+pλabcsvabcr=rriabcr+pλabcrE4

where,v is the voltage, i is the current, λ is the flux linkage, p is the first derivative operator, subscript s denotes variables and parameters associate with stator circuits and subscript r denotes the variables and parameters associated with the rotor circuits. a, b and c represent the three phases. r s and r r are diagonal matrices each with equivalent nonzero elements and

(fabcs)T=[fasfbsfcs](fabcr)T=[farfbrfcr]E5

where f represents either voltage, current or flux linkages. For a magnetically linear system, the flux linkages may be expressed as

[λabcsλabcr]=[LsLsr(θm(t))LsrT(θm(t))Lr][iabcsiabcr]E6

where L s and L r are the windinginductances which include the leakage and magnetizing inductances ofthe stator and rotor windings, respectively. The inductanceL sr is the amplitude of the mutual inductances betweenthe stator and rotor windings. L s and L r are constants and L sr is a function of the mechanicalrotor position, θ m (t). Details of the variables are described in (Krause et al., 1994).

The vast majority of induction motors used today are singly excited,wherein electric power is transformed to or from the motor throughthe stator circuits with the rotor windings short-circuited.Moreover, a vast majority of single-fed machines are of thesquirrel-cage rotor type. For a squirrel cage induction motor,v abcr = 0. Substituting equation (7) in equation (5),

vabcs=rsiabcs+Ls(piabcs)+(pLsr(θm(t)))iabcr+Lsr(θm(t))(piabcr)0=rriabcr+(pLsrT(θm(t)))iabcs+LsrT(θm(t))(piabcs)+Lr(piabcr)E7

In considering the steady state form of equation (8) we are mixing the frequency and time domain formulations for the sake of simplicity. Adhering to strict frequency or time domain representations provides the same qualitative results but it complicates the equations. The following steady state representation of equation (8) is obtained:

V˜s(t)=(rs+jωsLs)I˜s(t)+(jωsLsr(θm(t)))I˜r(t)0=jωrLsrT(θm(t))I˜s(t)+(rr+jωrLr)I˜r(t)E8

where, V s is the stator voltage, I s is the stator current, I r is the rotor current and ω is the speed. In equation (9), assuming that(rr+jωrLr)is invertible,I˜r(t)can be expressed as

I˜r(t)=jωrLsrT(θm(t))rr+jωrLrI˜s(t)E9

Substituting equation (10) in equation (9),

V˜s(t)=(rs+jωsLs+ωsωrLsr(θm(t))LsrT(θm(t))rr+jωrLr)I˜s(t)E10
Assuming(rs+jωsLs+ωsωrLsr(θm(t))LsrT(θm(t))rr+jωrLr)is invertible,I˜s(t)=[(rs+jωsLs+ωsωrLsr(θm(t))LsrT(θm(t))rr+jωrLr)]1V˜s(t)I˜s(t)=[Z(θm(t))]1V˜s(t)E11

where Z is a function of the machine parameters which inturn are functions of the mechanical rotating angle of the rotor,θ m (t). Equation (12) represents a modulatorwherein the current spectrum will be composed of both the inputvoltage frequencies and also other frequency components due to themodulation. The modulated frequencies will appear as side-bands inthe current spectrum around each frequency component correspondingto the input voltage signal. Hence an induction motor can be generalized as a modulator. Any fault in the rotor of the induction motor or in the motorbearings would result in the generation of additional spatialirregularities. This would induce additional spatial harmonics inthe motor air-gap flux. These additional harmonics would modulatethe voltage frequencies and appear as sidebands in the statorcurrent spectrum. Higher order spectra are used to detect thesemodulated frequencies in the stator current spectrum.

4.2.2. Proposed fault isolation indicator

Higher-order spectra is a rapidly evolving signal processing areawith growing applications in science and engineering. The powerspectral density or the power spectrum of deterministic orstochastic processes is one of the most frequently used digitalsignal processing technique. The phase relationships betweenfrequency components are suppressed in the power spectrum estimationtechniques. The information contained in the power spectrum isessentially present in the autocorrelation sequence. This issufficient for the complete statistical description of a Gaussianprocess of known mean. However, there are practical situations wherethe power spectrum or the autocorre1ation domain is not sufficientto obtain information regarding deviations from Gaussian processes and thepresence of nonlinearities in the system that generates the signals.Higher order spectra (also known as polyspectra), defined in termsof higher order cumulants of the process, do contain suchinformation. In this study higher order spectra are used to detectthe phase relationship between harmonic components that can be usedto detect motor related faults. One of the most widely used methodsin detecting phase coupling between harmonic components is thebispectrum estimation method. In fact, bispectrum is used indetecting and characterizing quadratic phase coupling.

Consider a discrete, stationary, zero-mean random process x(n). The bispectrum of x(n) is defined as

B(w1,w2)=τ1τ2c(τ1,τ2)e[j(w1τ1+w2τ2)]E12

where

c(τ1,τ2)=E[x(n)x(n+τ1)x(n+τ2)]E13

where, E[.] denotes the expectation operator. A class of technique called “direct” can be used to estimate the bispectrum. This technique uses the discrete Fourier transform (DFT) to compute the bispectrum as follows:

B(k1,k2)=E[X(k1)X(k2)X*(k1+k2)]E14

where X(k) is the DFT of x(n). From equation (14), it can be concluded that thebispectrum only accounts for phase couplings that are the sum of theindividual frequency components. However, motor related faultsmanifest themselves as harmonics that modulate the fundamentalfrequency and appear as sidebands at frequencies given by|fe±mfv|, where f e is the fundamental frequency and f v isthe fault frequency. Hence, the bispectrum estimate given by equation (14) detects only half of the coupling, asit does not detect the presence of the other half given by thedifference of the two frequency components. Moreover, informationabout the modulation frequency has to be known to use thisbispectrum estimate correctly. Hence to correctly identify themodulation relationship, a variation of the modified bispectrum estimator also referred to as the amplitude modulation detector (AMD)described in (Stack et al., 2004) is used.

The AMD is defined as:

AM^D(k1,k2)=E[X(k1+k2)X(k1k2)X*(k1)X*(k1)]E15

From equation (15), it can be seen that both the sidebandsof the modulation are accounted for in the definition. Noinformation about the modulation frequency is utilized in computingthe AMD. This is very useful since the motor related faultfrequencies which modulate the supply frequency are very difficultto compute. These frequencies are dependent on the designparameters, which are not easily available. For example, the faultfrequency pertaining to a motor rolling element bearing depends onthe contact angle, the ball diameter, the pitch diameter, etc. Henceit is desirable to design an algorithm which does not require themotor design parameters. In this study, the AMD definition given by equation (15) is applied to the three phase motor current signals and to the three phase motor voltage signals to obtain the fault isolation indicator (FII).

4.2.3. Decision making

The average of the FII is computed and tracked over time. As mentioned in the previous subsection, since the FII is based on the model of the induction motor, it is only sensitive to faults that develop in the induction motor and insensitive to faults in the centrifugal pump. If a fault develops in the induction motor, spatial harmonics are generated that leads to the FII to increase over time as the fault severity increases. Hence if the FII increases beyond a threshold, then it can be concluded that the fault is in the motor and not the pump. At the same time, if a fault is detected and the FII does not increase over time, then it can be concluded that the fault is in the pump and not the motor. The determination of the threshold is similar to the procedure followed to determine the fault detection threshold described in the previous section.

5. Sample results

Various experiments in a laboratory environment were conducted to test and validate the detection and isolation capability of the developed method. Experiments were also conducted to test the number of false alarms that the method generates. In this chapter, results from a field trial and a sample result from the laboratory experiments are presented. For more details on the various laboratory experiments, refer to (Harihara & Parlos, 2008a, 2008b, 2010). The proposed fault detection and isolation method was applied in an industrial setup to monitor a boiler feed-water pump fed by a 400 hp induction motor. Since no specific motor and/or pump model or design parameters are used in the development of the algorithm, the algorithm could be easily scaled to the 400 hp motor-pump system. The induction motor is energized by constant frequency power supply and the motor electrical signals are sampled using the current transducers and voltage transducers that are standard installations. Fig. 5 shows an indicative time series plot of the per unit value of the sampled motor electrical signals and Fig. 6 shows the power spectral density of one of the line voltages and phase currents. As shown in Fig.6. it is very difficult to detect the presence of the fault just by inspecting the spectrum of the electrical signals. The sampled electrical signals are used as inputs to the proposed fault detection and isolation algorithm to determine the “health” of the system.

Fig. 7 shows the proposed FDIC for the data sets from the power plant. Note that the FDI that is obtained from the sampled signals is not used for monitoring purposes because this might result in the generation of false alarms as described in the previous section. The FDI is always compared to the model prediction,FD^Iand only the relative change is used for monitoring purposes. Hence only the FDIC is shown in the figure for illustrative purposes. The motor electrical signals were sampled at different points of time within a 7 month period. After “Sampling Point 6”, data was continuously sampled till the motor was shutdown. The first few data sets are used to develop the motor-pump system model. Once the model is developed the proposed fault detection method is used to monitor the “health” of the system. A load increase is detected and the designed method accounts for this load change and re-initializes the proposed FDIC. The developed algorithm detects the presence of a fault within the motor-pump system as evident by the FDIC exceeding the defined warning threshold. Once the fault is detected the data is used by the proposed fault isolation algorithm to identify which component within the system has developed the fault. Fig. 8 shows the FII over time. The first few data sets are used to model the induction motor and get a baseline response of the motor. Note that the FII increases over time even though the motor drawn current is constant. As mentioned in the previous section, since the FII is based on a model of the induction motor, it is only sensitive to faults in the motor and insensitive to faults in the pump. Since the FII increases over time, it can be concluded that the fault is indeed in the motor and not in the pump. The power plant performed a diagnosis of the motor after shutdown and found a fault in the motor bearing.

Figure 5.

Time series plot of the sampled motor electrical signals.

Figure 6.

Power spectral density of one of the line voltages and phase currents.

Figure 7.

Proposed fault detection method applied to data set from Texas A&M University Campus Power Plant detecting the presence of a fault in the motor-pump system.

Figure 8.

Proposed fault isolation method applied to data set from Texas A&M University Campus Power Plant detecting the presence of a motor fault.

Figure 9.

Proposed fault detection and isolation method as applied to data set from a laboratory experiment; (top) proposed fault detection indicator change; (middle) motor current RMS; (bottom) proposed fault isolation indicator.

Fig. 9 shows the sample result from one of the laboratory experiments conducted to validate the performance of the proposed method on the detection and isolation of pump related failures. In this case study, one of the pump bearings is degraded using electric discharge machining (EDM) process. AC current of about 8A to 12 A is passed through the test bearing to accelerate the failure process. The top portion of the figure shows the FDIC which detects the fault immediately following the AC current injection. The middle portion of Fig.9 shows the change in the motor current. As the pump bearing is damaged the work output of the pump reduces which in turn results in the decrease of the input mechanical power. The decrease in the input power leads to a decrease in the motor current drawn. The bottom portion of the figure shows the FII based on the proposed method. As can be seen, the FII does not increase beyond the baseline variation since the developed model is insensitive to pump related faults and only sensitive to motor faults. This leads to the conclusion that the fault is indeed in the pump and not in the motor.

6. Summary

A novel fault detection and isolation method was proposed to detect and isolate centrifugal pump faults. The developed method uses only the motor electrical signals and is independent of the motor and/or pump design characteristics. Hence this method can be easily applied to other motor-pump systems. The proposed algorithm is also insensitive to power supply variations and does not presume the “health” condition of the motor or the pump. The developed fault detection and isolation method was applied in a field trail and was successful in detecting and isolating faults.

Acknowledgments

The research described in this chapter was conducted at Texas A&M University, College Station, TX, USA. The authors would like to acknowledge the financial support provided by the State of Texas Advanced Technology Program, Grants No. 999903-083, 999903-084, and 512-0225-2001, the US Department of Energy, Grant No. DE-FG07-98ID13641, the National Science Foundation Grants No. CMS-0100238 and CMS-0097719, and the US Department of Defense, Contract No. C04-00182.

© 2012 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Masahiko Tani, Masakazu Hibi, Kohji Yamamoto, Mariko Yamaguchi, Elmer S. Estacio, Christopher T. Que and Masanori Hangyo (February 24th 2012). Low-Frequency Coherent Raman Spectroscopy Using Spectral-Focusing of Chirped Laser Pulses, Vibrational Spectroscopy, Dominique de Caro, IntechOpen, DOI: 10.5772/32734. Available from:

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