Experimental investigations on induction machine condition monitoring and fault diagnosis using digital signal processing techniques

Transcription

1 Electric Power Systems Research 65 (2003) 197/221 Experimental investigations on induction machine condition monitoring and fault diagnosis using digital signal processing techniques Sa ad Ahmed Saleh Al Kazzaz a, G.K. Singh b, * a Department of Electrical Engineering, University of Mosul, Mosul, Iraq b Department of Electrical Engineering, Indian Institute of Technology, Roorkee , India Received 15 February 2002; received in revised form 7 November 2002; accepted 25 November 2002 Abstract Condition monitoring is used for increasing machinery availability and machinery performance, reducing consequential damage, increasing machine life, reducing spare parts inventories, and reducing breakdown maintenance. An efficient condition monitoring scheme is capable of providing warning and predicting the faults at early stages. The monitoring system obtains information about the machine in the form of primary data and through the use of modern signal processing techniques; it is possible to give vital information to equipment operator before it catastrophically fails. The suitability of a signal processing technique to be used depends upon the nature of the signal and the required accuracy of the obtained information. Therefore, in this paper, signals obtained from the monitoring system are treated with different processing techniques with suitably modified algorithms to extract detailed information for machine health diagnosis. In this study, on-line analysis of the acquired signals has been performed using C, while MATLAB has been used to perform the off-line analysis. # 2002 Elsevier Science B.V. All rights reserved. Keywords: Induction machine; Fault; Condition monitoring; Diagnostic; Digital signal processing; Fourier transform 1. Introduction Predictive maintenance by vibration monitoring of electrical machine is a scientific approach that becomes the new route to the maintenance management [1 /4]. Electrical machines, even new ones, generate some level of vibration [5 /17]. Small levels of ambient vibrations are acceptable. However, higher levels and increasing trends are symptoms of abnormal machine performance. Machine vibration analysis becomes one of the important tools for machine faults identification. There are two types of analysis, time domain and frequency domain. The frequency domain analysis is more attractive one because it can give more detailed information about the status of the machine whereas; the time domain analysis can give qualitative information about the machine condition. Generally, machine vibration * Corresponding author. Fax: / address: gksngfee@iitr.ernet.in (G.K. Singh). signal is composed of three parts, stationary vibration, random vibration, and noise. Traditionally, Fourier transform (FT) was used to perform such analysis. If the level of random vibrations and the noise are high, inaccurate information about the machine condition is obtained. Noise and random vibrations may be suppressed from the vibration signal using signal processing techniques such as filtering, averaging, correlation, convolution, etc. Sometimes random vibrations are also important because they are related to some types of machine faults hence; there is a need to observe these vibrations also. Signals obtained from the transducers are in the form of continuous voltage or current signals. It is necessary to define their values at certain instants of time to be suitable for digital signal processing (DSP) applications. The obtained digital signal is an adequate substitute for the underlying continuous signal if the interval between the successive samples is sufficiently small. The sampling frequency must be twice the highest frequency compo /03/$ - see front matter # 2002 Elsevier Science B.V. All rights reserved. doi: /s (02)

2 198 S.A.S. Al Kazzaz, G.K. Singh / Electric Power Systems Research 65 (2003) 197/221 nents of the signal (according to Shannon s theorem) to avoid aliasing of high frequencies components in the low frequency region of the spectrum. In the present work, the sampling frequency has been selected to be four times the highest frequency component of the signal to prevent any possibility of aliasing and to ensure the complete reconstruction of the signal. In this paper, signals obtained from monitoring system as shown in Fig. 1, are treated with different processing techniques with suitably modified algorithms to extract detailed information for induction machine diagnosis. All the techniques used here for signal analysis and processing have been implemented in C and MATLAB software. On-line analysis of the acquired signals is performed using C, while MATLAB is used to perform the off-line analysis. 2. Nature of electrical machine faults The induction motor is considered as a robust and fault tolerant machine and is a popular choice in industrial drives. It is important that the measures are taken to diagnose the state of the machine as and when it enters into the fault mode. It is further necessary to do so on-line by continuously monitoring the machine variables. The reasons behind failures in rotating electrical machines have their origin in design, manufacturing tolerance, assembly, installation, working environment, nature of load and schedule of maintenance. Induction motor like other rotating electrical machine is subjected to both electromagnetic and mechanical forces. The design of motor is such that the interaction between these forces under normal condition leads to a stable operation with minimum noise and vibrations. When the fault takes place, the equilibrium between these forces is lost leading to further enhancement of the fault. The motor faults can be categorised into two types: mechanical and electrical. The sources of motor faults may be internal, external or due to environmental, as presented in Fig. 2. Internal faults can be classified with reference to their origin i.e. electrical and mechanical or to their location i.e. stator and rotor. Usually, other types of fault i.e. bearing and cooling faults refer to the rotor faults because they belong to the moving parts. Fig. 3 presents the fault tree of induction machine where the faults are classified according their location: rotor and stator. 3. Simulation of induction machine under healthy and fault conditions Modelling and simulation of electrical machine dynamics has attracted many researchers since the early days of electrical machine invention [18]. The fast advances in computing facilities and the improvement in numerical techniques have lead to improvement in accuracy and simulation efficiency. Mathematical models have been developed to include the effect of core loss, saturation effect, winding distribution, and inherent machine faults [18 /24] Dynamic analysis of induction motor For simulation of dynamic state, the choice of model is made on the basis of operating conditions as follows:. Machine operating from balanced sinusoidal supply under nominal voltages, and under/over voltages (phase variable model).. Machine operating from balanced non-sinusoidal supply obtained from inverter (stationary reference frame model).. Machine operating from unbalanced sinusoidal supply (instantaneous symmetrical component model). Fig. 1. Schematic diagram of the monitoring system.

3 S.A.S. Al Kazzaz, G.K. Singh / Electric Power Systems Research 65 (2003) 197/ Fig. 2. Sources of induction machine faults. Fig. 3. Popular induction machine faults and their causes.

4 200 S.A.S. Al Kazzaz, G.K. Singh / Electric Power Systems Research 65 (2003) 197/221. Machines operating from acquired (recorded voltages from monitoring system) supply voltages (phase variable model) Various test conditions used for simulation purpose In this work, three phase 5 hp induction motors were chosen for the simulation purpose because the same machines are used in the practical investigations. These machines were simulated (dynamic simulation) for different test conditions, which are as follows:. Transient performance of induction motor under nominal supply.. Transient performance of induction motor under unbalanced (including single phasing) supply.. Transient performance of induction motor with under-voltage and over-voltage supply.. Transient performance of induction motor under variable frequency sinusoidal supply.. Transient performance of induction motor under variable frequency non-sinusoidal supply.. Transient performance of induction motor under mechanical faults (rotor eccentricity, dry bearing, and faulty bearing due to ball defects) Induction motor health identification based on on-line machine modelling The developed monitoring system (Fig. 1) comprises of three transformers for voltage measurement, three Hall-Effect probes for current measurements, pulsetachometer for speed measurement and four thermocouples for temperature measurements. Signal conditioners are used for thermocouples to provide linearisation, amplifications and cold junction compensation, and for vibration transducer to provide excitation, filtering and amplifications. The data is transferred to the computer using 12-bit A/D converter that provides less than 0.1% error in amplitude of the acquired signal. The sampling frequency has been selected so that complete reconstruction of the signals can be achieved. The sampling frequency is adjusted to 2 khz for electrical variables, and 4 khz for vibration signals. The speed transducer output is in order of 1000 pulse per revolution. To obtain the motor speed, these pulses are counted for a specific period of time (200 ms) using an on-board 16-bit down counter. The system accuracy and performance is tested with the known inputs. The hardware along with the software allows the users to effectively monitor, store, and analyse machine variables. The system provides on-line display of voltages, currents, and temperatures (using C graphic facility) with simple data analysis or directly storing the acquired data for off-line data analysis and processing. A sampling frequency of the order 15 khz is achieved with the on-line display of the acquired data, and about 50 khz with the direct storing of the acquired data. Moreover, the change in sampling frequency; number of samples, range of display and number of machine variables is possible. The machine health identification can be obtained with the aid of the on-line monitoring system discussed above. In this system, three phase currents; three phase voltages and speed are recorded on-line and stored in computer memory. The recorded three phase voltages are fed to the developed machine model in order to calculate the machine currents and to predict the machine conditions. By comparing the actual recorded machine currents (recorded simultaneously with machine voltage) with the simulated currents, the machine conditions can be obtained qualitatively. One of the effective methods, which have been adopted recently to predict machine condition using machine currents, is Park s vector approach [25]. Here this method is employed to obtain the machine behaviour due to various supply conditions. 4. Signal analysis Signal analysis is used to extract some useful features of the signal i.e. mean value, mean square, root mean square (RMS) value and the crest factor. The signal detectors have been implemented by software using simple algorithms Implementation of root mean square In this study, the RMS value of the vibration signal is used for primary investigation of the machine health. The RMS values of the machine voltages and currents are used to detect the unbalanced supply conditions, and to differentiate its effect from the effect of the other types of faults. Table 1 represents the RMS values of the machine voltages and currents and the average speed for five identical machines running under same operating conditions. These values are used as input to the neural network based fault classifier. Table 2 represents the RMS values of radial and axial machine vibrations for five identical machines running under different conditions Implementation of Crest factor The Crest factor is the ratio of the peak value to the RMS value. It is meaningful where the peak values are reasonably uniform and repeatable from one signal cycle to another. The Crest factor yields a measure of the spikiness of a signal and is used to characterise signals containing repetitive impulses in addition to a lower

5 S.A.S. Al Kazzaz, G.K. Singh / Electric Power Systems Research 65 (2003) 197/ Table 1 RMS value of machine voltages, currents and average speed V 12 (V) V 23 (V) V 31 (V) I 1 (A) I 2 (A) I 3 (A) N (rpm) Machine Machine Machine Machine Machine level continuous signal. Crest factor is often used to indicate the rolling element bearing faults. It may be noticed that both radial and axial vibrations are affected by the machine condition, but it is difficult to recognise the type of the fault by using these values. These values can give qualitative information about the machine condition if it is compared with the vibration standard. The values of Crest factor of the vibration signal for healthy and faulty five symmetrical induction machines running at no load are represented in Table 3. The bearing faults clearly affect the value of the Crest factor but the change in Crest factor is small with dry bearing fault. Continuous monitoring of this factor along with historical records can provides useful information about the bearing condition. From the above tables, it may be noticed that some of the machine conditions have a significant effect on the RMS value of the vibration while the others do not, and the same is true with the Crest factor. However, using both the RMS value and the Crest factor for machine health identification may increase the system efficiency for diagnostics. 5. Signal processing techniques There are two approaches used for processing the signal; time domain and frequency domain. In time domain approach, the discrete time signal is directly analysed by one of the DSP techniques [26/29] such as filtering [30,31], averaging [2], convolution, correlation etc. In frequency domain approach, the signal is first transformed to the frequency domain using FT then, different methods of analysis such as averaging, convolution, power spectrum, cepestrum etc. can be applied. Different signal processing approaches have been used here to extract the salient features of the signals obtained from the machine. 6. Implementations of signal processing techniques in time domain Different aspects are available for time domain analysis such as; time period of the signal, the peak value reached by the signal, the average value of the signal, RMS value of the signal etc. The choice of such approaches depends on the nature of the signal and the required information. In this section, some of the DSP techniques are introduced with their implementations with the data of vibration and electrical variables Implementation of signal averaging Time domain averaging is effective in suppressing signals that are not correlated within the averaging period. To get good results, it is necessary to know the repetition frequency precisely and sampling the signal with an integer number of samples per period. Noise p reduction goes as 1= ffiffiffiffi N ; greatly increasing the signal to noise ratio. In machinery diagnostics using the vibration signal, this approach has some serious drawbacks. The underlying assumption is that the shaft rotation rate is constant. This is not the case in rotating machines where there is variability in the shaft speed, which broaden the spectral peaks and gives poor results. This Table 2 RMS value of radial and axial vibration for different machine conditions RMS vibration (radial) (g) RMS vibration (axial) (g) M/C 1 M/C 2 M/C 3 M/C 4 M/C 5 M/C 1 M/C 2 M/C 3 M/C 4 M/C 5 Healthy condition Unbalanced supply Single phasing Mechanical unbalanced Faulty bearing (dry) Faulty bearing (ball defect)

6 202 S.A.S. Al Kazzaz, G.K. Singh / Electric Power Systems Research 65 (2003) 197/221 Table 3 Crest factor of radial vibration for different machine conditions Machine 1 Machine 2 Machine 3 Machine 4 Machine 5 Healthy condition Unbalanced supply Single phasing Mechanical unbalanced Faulty bearing (dry) Faulty bearing (ball defect) approach assumes that the shaft rate is known with a good deal of precision. Some measurement error in the shaft rate that serves to mistune the comb filter giving poor results is always expected. Hence, it is required that the data acquisition system should have adaptable sampling rate according to the current shaft rate. This requirement is difficult to meet in practice. Some of these problems have been previously addressed and various solutions have been proposed. One solution is to synchronise the vibration signal with a tachometer signal, where the tachometer signal marks the beginning of each averaging period. The data is then, ensemble averaged over the periods marked by the beginning of the tachometer pulse. This approach gets around the problems associated with the ability to adaptively change the sampling rate. Another alternative is to take a single vibration measurement at an arbitrary sampling rate and do irregular resampling to interpolate the signal. For voltage and current signals, this approach is successfully implemented to clean up the signal from noise. The reference point is precisely specified and an average of 18 runs gave good results. Fig. 4 shows the voltage waveforms of different runs and their average where the improvement in the signal can be observed clearly. Then the obtained signal is transformed to the frequency domain and treated with different signal processing and analysis techniques Implementation of correlation Correlation between two signals is used to obtain the similarity between them. Correlation function of the baseline and real time signals is used to investigate their relationship. Vibration, current and voltage signals of a healthy machine are considered as baseline signals. For primary investigation, two segments of a long vibration signal are correlated to obtain their similarity, as shown in Fig. 5. The correlation function of the vibration signals obtained from different runs with the same conditions is shown in Fig. 6. The figure shows that the correlation among them is high. The correlation between a baseline vibration signal and a faulty bearing vibration signal is shown in Fig. 7. It can be concluded that even the vibration signals obtained from the same machine running at the same condition, the correlation among them is not very high. This is due to the presence of noise and random vibrations. The information obtained from the correlation function play an important role in selecting the input data of the neural network. For electrical quantities such as voltages and currents, better results are obtained by implementing the correlation function. Fig. 8 shows a couple of current signal and a couple of voltage signal obtained from the same machine under healthy and faulty conditions and the corresponding correlation functions Implementation of signal filtering Filters are used for two purposes, to attenuate the noise and undesired frequency components and to separate some individual frequencies or band of frequencies for their relation with the machine faults. The RMS value of selected frequency components or band of frequencies is used to obtain machine condition by comparing the obtained values with the corresponding reference values. As it is mentioned above, the filter characteristic plays the key role of obtaining the required resolution and accuracy of the analysis. For example, it is desired to pick up a frequency component related to a certain type of machine fault which is close to a dominant frequency component, i.e. supply frequency of 50 Hz and double rotational speed 48.5 Hz (for rotor speed /1455 rpm). Fig. 9 shows the vibration signal before and after using smoothing filter for removing the high frequency noise from the signal. Due to the fluctuations in the vibration signal, bands of frequencies rather than individual frequency components are used to identify the machine conditions. The band pass filter that met the above-mentioned requirements is achieved using FIR filter. Fig. 10 presents the results of band pass filtering of three frequency regions of the vibration signal. For the purpose of diagnosis, variable tuned band pass filter is used to separate four frequency regions related to some common types of machine fault. The filter is first tuned to pass the frequency band of 1/200 Hz, this band is related to the first and second harmonics of the bearing characteristic frequencies and shaft frequency, which is related to the mechanical unbalance. A narrow band

7 S.A.S. Al Kazzaz, G.K. Singh / Electric Power Systems Research 65 (2003) 197/ Fig. 4. Sample of machine terminal voltage with average of 18 runs. Fig. 5. (a and b) Two segments of radial vibration signal; (c) correlation among them.

8 204 S.A.S. Al Kazzaz, G.K. Singh / Electric Power Systems Research 65 (2003) 197/221 Fig. 6. (a and b) Two vibration signal obtained from healthy machine; (c) correlation among them. filter is tuned to pass a frequency band of 2f9/4 Hz(f/ supply frequency) i.e. 96 /104 Hz for 50 Hz supply frequency. This band indicates the supply conditions i.e. unbalanced supply, turn-to-turn short, and single phasing. The third frequency band is selected by the filter to pass a frequency band of 220/400 Hz, which covers the high order harmonics of bearing characteristic frequency. The filter is then tuned to pass a frequency Fig. 7. (a and b) Two vibration signal obtained from healthy and faulty machine; (c) correlation among them.

9 S.A.S. Al Kazzaz, G.K. Singh / Electric Power Systems Research 65 (2003) 197/ Fig. 8. (a and b) Terminal voltages of healthy and faulty machine; (c) correlation among them. (d and e) Line currents of healthy and faulty machine; (f) correlation among them. Fig. 9. (a) Vibration signal of faulty machine; (b) filtered version.

10 206 S.A.S. Al Kazzaz, G.K. Singh / Electric Power Systems Research 65 (2003) 197/221 Fig. 10. (a) Vibration signal and filtered version; (b) (10/200 Hz) band pass; (c) (98/102 Hz) band pass; (d) (680/850 Hz) band pass. band of 550/950 Hz, this band is related to the vibration of electromagnetic origin i.e. rotor and stator slot harmonics. Later to filtering the mentioned bands, RMS values of these bands are calculated and then used in the diagnostic algorithm. Table 4 presents the RMS values of the selected bands for different machine conditions. It can be observed from the Table 4 that the RMS value of some frequency bands is affected with the supply condition while the others have relations with machine conditions. The effect of changing the operating frequency from 25 to 50 Hz in steps of 5 Hz on the RMS values of these frequency bands for healthy and faulty machines is also included and presented in Table 5. The change in the supply frequency leads to change in the machine speed, so far all speed dependent frequency harmonics and supply frequency dependent harmonics change their location in the spectrum. The RMS value of different frequency bands change with changing the supply frequency. For small frequency band such as the second band presented in Table 5, the change in supply frequency must be considered for correct use of the RMS value for diagnosis purpose. 7. Implementation of signal processing technique in frequency domain Frequency analysis of a signal highlights many important hidden features and extracts some useful information. The accuracy of information extraction depends upon the nature of the signal and the method of analysis. In the present work, FTs and short term FT Table 4 RMS value (g) of selected frequency bands of radial vibration Healthy condition Unbalanced supply Single phasing Mechanical unbalanced Faulty bearing (dry) Faulty bearing (ball defect) 1 /200 Hz /104 Hz /400 Hz /950 Hz

11 S.A.S. Al Kazzaz, G.K. Singh / Electric Power Systems Research 65 (2003) 197/ Table 5 Effect of varying the supply frequency on the RMS value (g) of selected frequency bands of the vibration signals Supply frequency (Hz) Healthy machine Faulty machine 1 /200 Hz 96/104 Hz 220/400 Hz 550/950 Hz 1 /200 Hz 96/104 Hz 220/400 Hz 550/950 Hz (STFT) are used to analyse the vibration, current, and voltage signal in frequency domain Implementation of spectrum averaging The implementation of signal averaging for vibration signal in time domain has serious drawbacks due to the instability of the signal and the difficulties in getting a reference point. However, spectrum averaging (or scan spectrum averaging) is used to obtain the averaging of a long signal in frequency domain with adequate results. The long signal is divided into number of equal length segments and then the spectrum of each segment is obtained. The average spectrum is then obtained by adding all the spectrums and dividing by the number of segments. Fig. 11 shows the vibration spectrum of three segments and the average spectrum of 35 segments. It is noticed that the effect of random vibrations and noise are eliminated in the average spectrum. The average spectrum is considered for the implementation of harmonics analysis method. Scan spectrum averaging method is also applied for vibration signals obtained by repeating the same experiment for number of times at same conditions. Fig. 12 shows the vibration spectrum of three runs and the average spectrum of nine runs of an induction machine running under same conditions. Fig. 11. (a, b and c) Spectrums of a windowed long vibration signal; (d) average of 35 spectrums.

12 208 S.A.S. Al Kazzaz, G.K. Singh / Electric Power Systems Research 65 (2003) 197/221 Fig. 12. Vibration spectrums of different runs and average of nine runs. This approach has one drawback that is the effect of window transitions. However, due to the finite length of the window function, the ends of the convoluted vibration signal may be distorted. Overlapping between the windows may reduce this effect Implementation of correlation Correlation in frequency domain can be achieved by direct multiplication of the two spectrums after taking the conjugate of one of them. The spectrum of the same signals used in the implementation of the correlation in time domain is used here as in the Figs. 13/16. It can be noticed that the results of correlating two vibration signals in frequency domain give more information about the similarity of the two spectrums, in comparison with same signals in time domain. Although, implementing correlation with vibration signal does not give adequate results due to change in the signal spectrum. For current and voltage signals correlation coefficient are more beneficial Implementation of signal filtering Signal filtering in frequency domain is much easier to implement than in time domain. It is simply achieved by multiplying the spectrum of the signal with that of the rectangular window. The width of the window is equal to the bandwidth of the filter, while the centre frequency of the window specifies the location of the filter in the band. One of the main advantages of this technique is the possibility of filtering only one frequency component. The accuracy of the filtering depends upon the resolution of the spectrum. Fig. 17 shows the vibration spectrum of a healthy induction machine before and after filtering some frequency components using variable length rectangular window. The spectrum of the machine current before and after filtering the fundamental frequency is shown in Fig. 18. The best results are achieved when the filtered spectrum components are equal to integer multiple of the frequency resolution Spectrum analysis In the present work, FFT algorithm is used to perform discrete Fourier transform (DFT) for the vibration, voltage, and current signals. The time domain signals and their spectrums are shown in Fig. 19. For each type of signal, there are different techniques for extracting the important features for diagnosis.

13 S.A.S. Al Kazzaz, G.K. Singh / Electric Power Systems Research 65 (2003) 197/ Fig. 13. (a and b) Spectrums of two vibration segments of healthy induction machine; (c) cross correlation among them. Fig. 14. (a and b) Vibration spectrums of healthy induction machine obtained from different runs; (c) cross correlation among them.

14 210 S.A.S. Al Kazzaz, G.K. Singh / Electric Power Systems Research 65 (2003) 197/221 Fig. 15. Vibration spectrums of (a) healthy machine, (b) faulty machine, (c) cross correlation among them. Fig. 16. (a and b) Voltage spectrum of two machine; (d and e) the corresponding line currents; (c and f) the voltages and currents correlation, respectively.

15 S.A.S. Al Kazzaz, G.K. Singh / Electric Power Systems Research 65 (2003) 197/ Fig. 17. (a) Healthy machine vibration spectrum and the filtered version; (b) (10/200 Hz) band pass; (c) (98 /102 Hz) band pass; (d) (680/850 Hz) band pass. Fig. 18. (a) Machine current spectrum; (b) spectrum after filtering the fundamental harmonic.

16 212 S.A.S. Al Kazzaz, G.K. Singh / Electric Power Systems Research 65 (2003) 197/221 Fig. 19. (a, b and c) Voltage, current and vibration signals, respectively; (a?, b? and c?) the corresponding spectrums Vibration analysis In order to achieve earliest possible recognition of the defect in a machine, a comparison of the spectrum of the machine under study with the spectrum of a typical (healthy) one must be performed. However, four approaches of comparison are used Narrow band analysis. This type of analysis is performed using high-resolution in frequency. By increasing the resolution of the spectrum, more details about the frequency contents of the signal can be achieved, but the stability of the spectrum becomes poorer. Frequency resolution of 0.1, 1.0, 2.0 Hz is used here for the purpose of comparison. Fig. 20 shows the vibration spectrums with different resolutions. It can be noticed that, although high frequency resolution gives more detail information, any small change in the signal (i.e. small random vibration and noise) makes a difference in the individual lines and thereby in the spectrum as a whole. In addition, if there is a change in the rotation speed, all the rotation dependent frequency components will change in the spectrum (i.e. 1% change in the speed cause the 1 / components to shift its location by 1 and so on), so that direct comparison is difficult to achieve. Frequency resolution of 1 Hz for vibration signal gave acceptable detail and stable spectrum for most of the studied cases. However, from the literature some frequency components related to some types of faults, and from the calculation of the machine vibration another set of frequency components are obtained. The amplitudes of these components are used to specify the degree of fault for the certain operating condition. The vibration spectrums for mechanical unbalance, supply unbalance and single phasing are presented in Fig. 21. It can be observed that the first rotational harmonic has a dominant value in the three spectrums, which is higher than the baseline value. In this case, it is difficult to recognise the type of the fault, hence another frequency components must be considered for comparison. In addition, by including another machine quantities such as currents and voltages and the expert s knowledge, enhancement of the system ability for diagnosis may be achieved Variable band harmonic analysis. In this approach, selected frequency components from the vibration spectrum are used for comparison. It has been noticed that the locations of these selected components rapidly change. These changes are due to the random vibrations and change in machine speed. Hence, narrow band analysis may not be suitable for the comparison between the two spectrums. In the present approach, a small band of frequency is considered instead of individual frequency components. The bands used are

17 S.A.S. Al Kazzaz, G.K. Singh / Electric Power Systems Research 65 (2003) 197/ Fig. 20. (a, b and c) Vibration spectrums for different value of frequency resolution 0.1, 1 and 2 Hz, respectively; (a?, b? and c?) zoomed version. Fig. 21. Vibration spectrum correspond in various machine conditions; (a) healthy machine; (b) mechanical unbalanced; (c) unbalanced supply; (d) single phasing.

18 214 S.A.S. Al Kazzaz, G.K. Singh / Electric Power Systems Research 65 (2003) 197/221 a percentage of the order of the frequency component. Different percentages of the frequency components are tried to select the optimum value that gives acceptable results for the comparison between different types of machine fault. For example, if the selected frequency is equal to 100 Hz and the percentage is 2%, the band will include the frequency components from 98 to 102 Hz and so on for other frequencies. After selecting the width of the band, three procedures are used for obtaining the compared value of the band. The first one is achieved by calculating the average value of the band components and then using this value for comparison with the corresponding value of other spectrums. The second one is achieved using the highest peak in the band. The third procedure uses the energy of the band for comparison with the energy in the corresponding band in the reference spectrum. It is observed that all the procedures give good results in the case of medium and high degree of faults. For small degree of fault, uncertain information may be obtained, especially if the level of random noise is high and there is fluctuation in the speed, load and supply voltage Band frequency measurement. This approach can be used to give a primary investigation about the status of the machine. Bands of frequency in the vibration spectrum are selected according to the origin of the fault. The RMS value of these bands is used to specify the degree and the origin of the faults by comparing them with the corresponding bands in the reference spectrum. From the literature and experimental observations, four frequency bands are selected to cover the vibration harmonics of mechanical and electromagnetic origin. The problem in using this approach arises with some type of faults, where the vibration harmonics and its multiples may cover wide range of the spectrum especially at high degree of faults and that may increase the uncertainty of the obtained information Spectrum masking. As mentioned earlier, if there is a level of random change in the signal or change in the speed, narrow band analysis approach may fail to provide accurate information. This problem may be recovered with the help of Spectrum Masking technique. The technique involves two steps. In the first, a new spectrum is formed by adding the energy present in a number of narrow frequency bands of the original spectrum. The width of narrow frequency band is equal to a fixed width. Fig. 22 shows the original spectrum and the new-formed spectrum. The bands of the new spectrum must be wide enough to encompass the variation in the signal. From the new generated spectrum, another spectrum is generated to compensate the change in the speed. Taking each segment of the spectrum and pushing one bandwidth to either side as shown in Fig. 23 obtain this spectrum. The minimum level of the spectrum components is fixed to a threshold level. The new spectrum is used successfully to compensate up to 3.5% change in the speed. If the speed changes are more than the amount corresponding to the selected bandwidth, the vibration harmonics will be lied outside the limits of the mask. Fig. 24 presents the spectrum masking for healthy machine and for the machine with defected bearing. The comparison using these two spectrums is much easier than using the original spectrums Voltage and current signals Frequency analysis of electrical variables of the machine has been used to predict the machine condition. Current harmonics can be related to most types of the machine faults [32 /34]. In the present investigation, current harmonics are examined to demonstrate the relation between current harmonics and the machine health. Fig. 25 shows the current waveform and its spectrum for faulty and healthy induction machine. It can be noticed that the spectrum becomes smoother in the case of a fault. This is due to the interaction (adding and subtracting) between the mmf space harmonics and the harmonics developed by some types of machine fault [35]. The effect of feeding the machine from PWM inverter with filtered output on the machine current waveform and the spectrum for healthy and faulty condition are given in Fig. 26. It can be noticed that in the case of non-sinusoidal supply such as PWM inverter it is difficult to distinguish between the current harmonics due to the supply harmonics and the harmonics originated by the machine conditions. It may be noticed that a precise monitoring system is needed to distinguish between the low-level frequency components and the high-level supply frequency component. This method needs to adjust the sampling frequency so that the interest frequency component is equal to integer multiple of the frequency resolution [36,37]. Terminal voltage harmonics are examined to find the exact value of the supply frequency, which is needed by the diagnostic algorithm. In addition, the harmonic analysis of the terminal voltage is used to predict the harmonics of the machine. This is achieved by recording the machine terminal voltage directly after switching off the machine i.e. induced machine voltage. The induced voltage harmonics are same as air-gap flux harmonics that can be related to the machine condition. Fig. 27 shows the induced voltage waveform of healthy and faulty machine and their spectrum. For the same reason mentioned above the faulty spectrum of the induced voltage became smoother. The harmonics analysis of the machine vibration can be used to detect wide range of the machine faults. The change in the amplitude of current and voltage harmo-

19 S.A.S. Al Kazzaz, G.K. Singh / Electric Power Systems Research 65 (2003) 197/ Fig. 22. Spectrum masking of the vibration signal using (a) band energy; (b) peak value. Fig. 23. Vibration spectrum with spectrum masking.

20 216 S.A.S. Al Kazzaz, G.K. Singh / Electric Power Systems Research 65 (2003) 197/221 Fig. 24. (a) Vibration spectrum and the modified mask of healthy induction machine; (b) vibration spectrum and the modified mask of faulty induction machine. Fig. 25. Effect of machine condition on current harmonics (a) healthy machine; (b) machine with faulty bearing.

21 S.A.S. Al Kazzaz, G.K. Singh / Electric Power Systems Research 65 (2003) 197/ Fig. 26. Effect of machine condition on the current harmonics under non-sinusoidal supply. (a) Healthy machine; (b) machine with faulty bearing. Fig. 27. Effect of the machine condition on the induced voltage harmonics. (a and b) Induced voltage of healthy and faulty machine, respectively; (a? and b?) the corresponding spectrums.

22 218 S.A.S. Al Kazzaz, G.K. Singh / Electric Power Systems Research 65 (2003) 197/221 nics due to the machine faults have a small level and the harmonic are very close to other spectrum harmonics, hence can not be easily detected. The harmonic distortion in current and voltage is used to assist the diagnostic task in obtaining the machine condition Implementation of short term Fourier transform STFT is used to estimate the frequency contents of the non-stationary signals. STFT is dividing the signal into a small segment using a window function (W) and perform FFT for each segment. However, the vibration signal picked up from the machine has two components; stationary and non-stationary. Non-stationary part has no relation with most of the studied faults. STFT is applied to eliminate the non-stationary part by adding the spectrum of all windowed segments and dividing it by the number of segments as in case of spectrum averaging. The window function used in this investigation is the rectangular window, which is simple to implement and has some useful characteristics such as narrow main lobe 4p/(2N/ 1). The spectrum of the rectangular is shown in Fig. 28. It can be noticed that there are several side lobes at both ends of the window. These side lobes may give uncertain information in the vibration spectrum and cause Gibbs phenomena [29]. Making overlap between the successive windows can reduce this effect. In this investigation, the number of samples used is and the width of window is equal to 4000 samples with overlap equal to 1200 samples. Then an average spectrum is obtained using the spectrums of the windowed data. Fig. 29 shows the vibration signal, the spectrums of the signal of different window locations and the average spectrum. Fig. 29(b) shows that the effect of random vibration and the noise has reduced in the average spectrum. This procedure is repeated for all experimental data and the obtained spectrums are used to extract the required information and to state the health of the machine. 8. Isolation of random vibrations Any vibration signal obtained from electromechanical systems contains a level of random changes. These random changes in the measured signal may be due to the random vibrations. These random vibrations can be related to the health of the machine for some faults such as dry bearing fault or bearing ageing. If these random vibrations could be isolated from the measured signal, useful information about bearing health may be obtained. From experimental observations, it is noticed that there are some changes in the level of RMS values of vibration signals obtained from the successive segments of a long record signal or from repeatable tests Fig. 28. Frequency response of the rectangular window.

23 S.A.S. Al Kazzaz, G.K. Singh / Electric Power Systems Research 65 (2003) 197/ Fig. 29. (a) Implementation of STFT using moving rectangular window; (b) average spectrum. performed on induction machine subjected to dry bearing fault running under similar conditions. The presence of dry bearing fault at an early stage introduces a small level of randomness in the vibration signal. Therefore, it is difficult to isolate these random vibrations by traditional signal processing techniques. The difficulty in discovering such fault is that neither the variation in vibration level nor additional spectrum components is detectable In order to isolate these random vibrations, the algorithm presented in Fig. 30 is used with some modifications. In this algorithm, the RMS value of each segment of the vibration signal is calculated but not averaged as shown in Fig. 30. These values are obtained from the same machine running with the same condition. The fluctuation in the RMS values is nothing but the random vibrations. 9. Conclusion In this paper, the treatment of raw data obtained from physical machine parameters are presented. The implementations of various DSP and analysis techniques in time and frequency domain with different machine variables are given. Signal detectors such as mean square value and crest factor are used with the voltages, currents and vibration signals in time domain. In addition, signal filtering, signal averaging and correlation are used in time and frequency domain simultaneously. FTs and STFTs are used to present the time domain signal in frequency domain. The obtained vibration spectrum is analysed using narrow band, variable band, selected band and spectrum masking approaches. The data implemented with mentioned techniques covers different operating conditions of the machine under test. The obtained information is used as input to the neural network. Time domain analysis is used to obtain the machine condition qualitatively using correlation coefficient, RMS value, and crest factor. For primary investigation, time domain analysis provides a rough figure, and is not appropriate for fault classification and ranking. Frequency domain analysis of the vibration signal provides detailed information. The vibration harmonics are related to types of machine faults. The machine condition can be obtained by comparing the amplitude of these harmonics with those obtained from corresponding ones in the healthy machine. The traditional treatment of vibration spectrum fluctuations is the averaging, which may lead to hide some features of short duration. The alternative approach to such non-stationary vibration signal is the Wavelet transform that can provide useful information about any signal in time domain with different bands of

24 220 S.A.S. Al Kazzaz, G.K. Singh / Electric Power Systems Research 65 (2003) 197/221 Fig. 30. Isolation of random vibrations from different wavelet decompositions. frequencies. WT gives variable time resolution for different frequency bands rather than STFT, which gives constant resolution. The area of condition monitoring and diagnostic is very wide and includes many topics. It is suggested to make some improvements in the monitoring system through the use of the following:. Inclusion of mmf harmonics in induction machine model so that the relation between the machines space harmonics and vibration harmonics can be established. This can be made using machine permeance approach. An alternative to this is the modelling of electromagnetic behaviour of the machine using finite element approach.. Estimation of machine parameters (resistance and inductance) through on-line modelling of induction machine. The estimated values of machine parameters can give indication about the machine health through a comparison with healthy one.. Building a database for vibration harmonics using experimental and theoretical investigations for various size and design of standard of three phase induction motors. Through this data base, a new standard for vibration can be established instead of the traditional one, which depends upon RMS velocity of vibration rather than harmonic amplitude.. Employing expert system for fault diagnosis of induction motor using rules obtained from the connection weight of a supervised neural network and rules extracted from the heuristic knowledge. This combination of ANN knowledge and expert s knowledge may enhance the accuracy and efficiency of the monitoring system for diagnosis.

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