Recorded acoustic signals.
In this chapter, innovative predictive maintenance technique is described with the aim of highlighting the benefits of predictive maintenance compared to time-based maintenance. The proposed technique is applied to a specific problem that occurs when time-based maintenance is applied on grinding tables of the coal mill, in coal-grinding subsystem at the thermoelectric power plant ‘TEKO’, Kostolac, Serbia. Time-based maintenance provides replacement of grinding tables after certain number of working hours, but depending on the quality of the coal and grinding table itself, this replacement sometimes needs to be made before or after planned replacement. The consequences of such maintenance are great material losses incurred because of frequent shutdowns of the entire coal-grinding subsystem, as well as the possibility that the failure occurs before replacement. Innovative predictive maintenance technique described in the chapter is used for solution of this problem.
- predictive maintenance
- T2 control chart
- hidden Markov model
- thermoelectric power plant
- statistical process control
In today’s industry, application of the best maintenance strategies is a very important task in ensuring stability and reliability of technical systems. Numerous papers and books about different maintenance strategies can be found in literature, and almost everywhere the merits of predictive maintenance in regard to time-based maintenance are emphasized . Predictive maintenance extends the period of time during which the system functions well, decreases unnecessary shutdowns, reduces material losses and prevents catastrophic failures. Although this field of research is very much advanced with the development of highly sophisticated technologies, there is still a lot of room for improvement of the existing techniques and the development of new ones.
In this research, an innovative technique of predictive maintenance is proposed and applied to a specific problem that occurs at the thermoelectric power plant ‘TEKO’, Kostolac, Serbia. Namely, one of the key thermoelectric power plant components is the coal-grinding subsystem. When time-based maintenance is applied on grinding tables of the coal mill, grinding tables are replaced after certain number of working hours. Depending on the quality of the coal and grinding table itself, this replacement sometimes needs to be made before or after planned replacement. The only way to determine the condition of the grinding table is visual inspection, which implies the shutting down of the whole subsystem. Consequences of grinding table replacement after fixed time intervals are great material losses incurred because of frequent shutdowns of the entire coal-grinding subsystem. Also, there is a possibility that the failure will occur before replacement.
There is an ‘urban legend’ that experienced operators in industrial plants, such as thermoelectric power plants, can ‘hear’ the sounds in sound content from operational drives. Based on these sounds, they can recognize the detritions of specific elements that can wear out, such as mill-grinding tables. Also, in literature one can find that 99% of mechanical failures are foregone by some very noticeable indicators . Because of these facts, the idea came up for the recording of acoustic signals while coal-grinding subsystem is operational. In this way, it is easy to obtain condition-monitoring data which can be applied for predictive maintenance, and there is no need for shutting down the whole subsystem for obtaining the information about grinding table condition.
The proposed method is a trade-off between solutions already offered in the literature, and originality of the proposed algorithm is based on the selection of failure prognostic technique. The main goal of the proposed algorithm is the increase of energy efficiency at the thermoelectric power plant.
This chapter is organized as follows: In the next section, we describe the concept of predictive maintenance in detail. In Section 3, a description of the coal-grinding subsystem in thermoelectric power plant will be given. In Section 4, we present a new predictive maintenance technique. Section 5 contains the results. The last section is the conclusion, with the discussion about gain results.
2. Predictive maintenance
Nowadays, industrial processes are very complex and cannot be imagined without modern technologies, so highly sophisticated and very expensive maintenance strategies are needed. Consequences of inefficient maintenance are large material losses, and because of that it is necessary to constantly develop and improve the existing maintenance programmes.
Maintenance strategies were evolving during time. The first maintenance strategy was the
Diagnostics and prognostics are two very important aspects in predictive maintenance programme. Diagnostics deals with fault detection, isolation and identification after occurring of the fault. Fault detection indicates when something goes wrong in a monitored system, that is, it detects that failure has occurred. Fault isolation has a task to locate faulty component, whereas fault identification has a task to determine the nature of the fault when the fault is detected. Diagnostics has been developed for years, and today it presents very important area in engineering and automatic control [6, 7].
Prognostics deals with fault prediction, before the fault will occur. In other words, diagnostics is the posterior analysis of events, while prognostic is a priori analysis of events. Prognostics is more efficient in regard to diagnostics for achieving zero-downtime performances. On the other hand, diagnostics is necessary when failure prediction within prognostic fails and fault occurs. References about prognostics can be found in Refs. [8, 9]. Predictive maintenance can be used for diagnostics and prognostics, or both. Some newer references about predictive maintenance can be found in Refs. [10–12]. No matter what is the goal of predictive maintenance, three key steps must be followed for its implementation: (1) data acquisition, (2) data processing and (3) maintenance decision-making.
Data acquisition is the process of data collection from specific physical resources in order to implement predictive maintenance properly. This process is the key step in applying predictive maintenance, both for diagnostics and for prognostics. Collected data can be classified into two major categories:
The first step in data processing is data cleaning. This step is very important, because data (especially event data), which are entered manually always, have some mistakes. Without data cleaning, it is possible that diagnostics and prognostics will be inaccurate. The next step in data processing is data analysis. Different models, algorithms and tools for data analysis depend mostly from data type . Condition-monitoring data can be classified into three categories: (1) value type, (2) waveform type and (3) multidimensional type.
The last step in predictive maintenance programme is decision-making. Techniques for decision-making can be divided into two categories:
Here, we focus on prognostics. There are two types of prediction when we talk about
Traditional reliability approaches—prediction based on event data (experience) 
Integrated approaches—prediction based on event data and condition-monitoring data .
Every one of these approaches has some advantages and limitations. Combinations of these approaches are different according to their applicability, price, precision and complexity .
3. Description of the coal-grinding subsystem in thermoelectric power plant
Thermoelectric power plants are the largest producers of electricity in Serbia, contributing with more than 65% of the total electricity supply. In order to ensure their stability and operational efficiency, it is necessary to monitor their major subsystems and individual components. In this way, it is possible to detect any change in behaviour, or failure on time, which leads to the increase of energy efficiency and the reduction of the financial losses of the electric power industry.
One of the key thermoelectric power plant components is the coal-grinding subsystem. Its physical layout is shown in Figure 1. Raw coal enters the subsystem through a feeder and goes down a chute to the grinding table that rotates at a constant speed. The coal is then moved outward by centrifugal force and goes under three stationary rollers where it is ground. The outgoing coal moves forward to the mill throat where it is mixed with hot primary air. The heavier coal particles immediately move back to the grinding table for additional grinding, while lighter particles are carried by the air flow to the separator. The separator contains a large amount of particles suspended in the powerful air flow. Additionally, some of the particles drawn into the primary air-and-coal mix lose their velocity and fall onto the grinding table (as shown) for further grinding, while the particles that are fast enough enter the classifier zone. These particles are swirled by deflector plates. Lighter particles are removed as classified fuel in the form of fine powder that goes to burners, while heavier particles bounce off the classifier cone and fall back onto the grinding table for additional grinding. Both the separator and classifier contain a significant amount of coal. These coal masses, along with the coal on the grinding table and the three recirculating loads (primary, secondary and tertiary), play a key role in the dynamic performance of the mill [21, 22].
In this research, one such system at the thermoelectric power plant ‘TEKO’ (Serbia) is analysed. As it is previously described, the coal inside the mill is ground by impact and friction against the grinding table that rotates around the mill centre line (CL). The only way to determine the current condition of the grinding table is to shut down the entire subsystem and open it for visual inspection. This time-based maintenance method guarantees that grinding tables will be replaced before they become dysfunctional, but at a cost of frequent shutdowns. If inspection shows that grinding table replacement is not yet necessary, then significant material losses will incur. In Figure 2, two grinding tables are shown. On the left figure is a new grinding table, immediately after replacement, and on the right figure is a worn grinding table, straight before replacement.
In practice which is common on plant A1, at thermoelectric power plant ‘TEKO’, Kostolac, grinding tables are replaced every 1800 h. However, it often happens that because of the increased presence of limestone, sand and other impurities in coal, grinding tables become deteriorated already after 1400 h, or even shorter. In that case, weaker effectiveness of the mill is noticeable, it is ‘chocked’, and serious problem with regulation occurs in an attempt to regulate the temperature of air mixture and pressure of fresh steam in front of the turbine. This appearance has for consequence significant misbalance of temperature distribution inside the firebox, which has negative influence on increased water injection in fresh steam, knockdown of coefficient of boiler efficiency and so on. In such conditions, usually, mill must be stopped unplanned for grinding table replacement and that incurs financial losses. Because of that, system which offers predictive maintenance is of great importance.
4. Proposed new predictive maintenance technique
The proposed solution to described problem is based on predictive maintenance. In this research, for the last step in predictive maintenance, the condition-monitoring data approach is chosen. This approach can be divided into two main categories:
As it is described earlier, the first step in predictive maintenance programme is data acquisition. In this research, acoustic signals recorded in the vicinity of the mill were used to detect the condition of the mill. The acoustic signals were acquired from a coal mill at the ‘TEKO’ thermoelectric power plant, while it was operational. The main mill rotation frequency was about 12.5 Hz and the mill from which the signals were acquired had 10 impact plates.
Namely, in the literature it can be found that failure information is hidden in the spectral characteristics of vibration signals , but it has been demonstrated that in some cases acoustic signals are equally informative. In 2001, Baydar conducted a parallel analysis of the frequency characteristics of vibration signals and acoustic signals to detect various types of failures of rotary components, concluding that both signals can be used equally effectively . The present research uses acoustic signals because they are simpler and less costly to record than vibration signals. They can also be acquired without interfering with mill operation because they are recorded externally.
The acoustic signals were acquired by means of a directional microphone at a distance of several millimetres, while the coal-grinding subsystem was operational. Recording of these signals is performed at the low altitude in thermoelectric power plant, where acoustic contamination is highly expressed. Because of that, special system for microphone fixation is projected, at a distance of several millimetres from the walls of analysed mill, so the power of useful signal could be multiple higher than the power of contaminating acoustic sources as neighbouring mills, feed pumps, surrounding valves and so on. The sampling frequency of recorded acoustic signals was 48 kHz. Data acquisition was conducted every 2 weeks on average, and it lasted for several minutes. Table 1 shows the dates of recording, the dates of grinding table replacement and the duration of each signal. For faster implementation of the algorithm, the sampling frequency was decimated from 48 to 4.8 kHz, and the duration of the analysed signals was 1 min.
|Date of acquisition||Signal duration||Time since last maintenance|
|2 February 2012||10 min 51 s||14 days|
|24 February 2012||8 min 8 s||36 days|
|1 March 2012||8 min 8 s||42 days|
|15 March 2012||7 min 3 s||54 days|
|30 March 2012||6 min||6 days|
|5 April 2012||5 min||12 days|
|19 April 2012||6 min||26 days|
We can see from Table 1 that the whole time period from the moment of grinding table replacement until the moment when grinding tables are worn is covered. After the first cycle of acoustic signal recording, three more recordings were performed after grinding table replacement. In this way, based on recorded acoustic signals, coal-grinding subsystem data are collected in different states. A large base of condition-monitoring data is obtained (without disturbing coal-grinding subsystem while it is operational) which can be further processed.
The second step in predictive maintenance is data processing. Given that collected data are acoustic signals, they are classified as waveform type of data. In order to overcome disadvantages encountered when such data are analysed in time domain and frequency domain , these data are analysed in time-frequency domain. A spectrogram was used to assess the acoustic signals in the time-frequency domain, which represented the spectral components of the signals in three dimensions very well: time information along the horizontal axis, frequency information along the vertical axis and amplitude depicted by a colour-coded scale. Colour intensity illustrated the strength of the spectral components. Figure 3  shows the spectrogram of an acoustic signal recorded on 30 March 2012, 6 days after grinding table replacement.
Figure 3 clearly shows the dominant frequencies, and indicates that they are the high harmonics of the basic frequency of mill rotation, which was
After data acquisition, it was necessary to extract proper characteristics of the recorded acoustic signals in the frequency domain, in order to obtain vector of observations for analysis with
The extracted quality characteristics in the frequency domain are the values of
Accordingly, the 14-dimensional vector of observations is formed at each time point:
Coordinates of the vector
The last step in predictive maintenance programme is maintenance decision-making. As it is described in the beginning of this section, data-driven technique is chosen, that is, it is decided that the input of the sequence of observations be analysed with
After obtaining the vector of observations,
The first step in constructing the control charts requires an analysis of preliminary data, which are under statistical control. This step is called Phase I, and data used in this phase are called the historical data set. In Phase II, the control chart is used to monitor the process by comparing the sample statistic for each successive sample as it is drawn from the process to the control limits established in Phase I [28, 29].
A multivariate analysis with Hotelling
The control limits in Phase II are
where is the upper α percentage point of the chi-squared distribution with
where is the upper α percentile of beta distribution with parameters
According to relation (5), the time sequence of
will be used for further estimation of system states. However, if this vector had been introduced as observation in
After the samples were coded as described above, the next step was to construct the
The transition matrix
The sequence of initial states
There are three fundamental problems that can be solved by means of
Figure 5  shows how the proposed algorithm for predictive maintenance is organized. For the purpose of the practical implementation of the proposed method, it should be clarified that certain activities are realized only once (like
In this chapter, gained results after applying the proposed technique for predictive maintenance on described problem at thermoelectric power plant will be presented. As it is previously explained, after data acquisition and feature extraction from recorded acoustic signals,
The acoustic signal recorded on 30 March 2012 was used for and
From Figure 6, we can see that the values follow chi-squared distribution, that is, the figure shows approximately linear trend along the line of 45°, except the last few points which are slightly away from the projected trend line. Before
It is apparent from Figures 7–9 that the number of points above the
The difference in the number of points above the
According to Table 2, we can conclude that with the choice of parameter
|Date of recording||Number of weeks after grinding table replacement||Number of points above UCL (%), ||Number of points above UCL (%), ||Number of points above UCL (%), ||Number of points above UCL (%), |
|2 February 2012||2 weeks||1.43%||2.14%||2.46%||5%|
|24 February 2012||5 weeks||68.27%||79.5%||83.78%||88.41%|
|15 March 2012||8 weeks||84.85%||90.91%||92.87%||95.54%|
|05 April 2012||2 weeks||16.75%||27.63%||32.98%||43.14%|
|19 April 2012||4 weeks||57.58%||70.05%||74.87%||81.64%|
The final step of the proposed algorithm was to construct the
It is apparent from Figure 11 that the
Based on the presented results, we can make several conclusions. Firstly, the assumption set at the beginning of this research, that useful information from spectral components of acoustic signals can be extracted is confirmed. Based on this information, the condition of rotating elements of the mill can be recognized. As it is previously explained, in the literature there are mostly preferred vibration signals in regard to the acoustic signals, when we talk about informative content. Given that the recording of acoustic signals is much cheaper than the recording of vibration signals, and processing of acoustic signals is much simpler from vibration signals processing, confirmation about informative content of acoustic signals is very important.
The originality of the proposed method is a combination of control charts and
As it is previously described, in the case of failure prognostic, in literature the most common approach is the first approach, that is, the estimation when the fault will occur (
The advantage of the proposed method is that it is non-invasive, because for the acquisition of condition-monitoring data it is not necessary to interrupt coal-grinding subsystem operation and shut down the whole subsystem. Another advantage is that it is based on acoustic signals processing which are simpler for processing and acquisition in regard to vibration signals. Software realization of the proposed algorithm is not too much complex and it is not time consuming when
A shortcoming of this method is the recording of acoustic signals in the presence of the unavoidable noise, which can influence on the accuracy of the results. Presented results are gathered
Further direction in this research would be the making of an adaptive system which would be adjustable to new statistics which are consequences of components ageing, not just the condition of grinding tables plates. Also, significant study could be made when condition-monitoring data would be recorded vibration signals, for comparative analysis with acoustic signals. Additional event data could upgrade the proposed method in combination with condition maintenance data. Some future research could be to make optimal maintenance policy in thermoelectric power plant, according to gain results.