Analysis Of Induction Motor With Broken Rotor Bars Using Discrete Wavelet Transform Princy P 1 and Gayathri Vijayachandran 2
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1 Analysis Of Induction Motor With Broken Rotor Bars Using Discrete Wavelet Transform Princy P 1 and Gayathri Vijayachandran 2 1 Dept. Of Electrical and Electronics, Sree Buddha College of Engineering 2 Dept. Of Electrical and Electronics, Sree Buddha College of Engineering, Kerala Abstract This paper presents the analysis of startup transient current signals of squirrel cage induction motor with broken rotor bars using Wavelet Transform (WT). At light load there occurs a difficulty to distinguish a faulty rotor with a healthy rotor. Since the length of the startup transient is only a few cycles depending on the load, all the information regarding the machine s condition will be present within this transient. Because the Fourier Transform is integrating over infinity, all the information that exists during the start up transient will be lost. This poses the problem that the Fourier Transform cannot be used. The Wavelet Transform is used to analyze the start up transient. Also the wavelet technique helps to provide additional information about its transient characteristics which cannot be detected using Fast Fourier Transform (FFT). Finally, the resulting wavelet coefficients are then compared with different degrees of broken rotor. Keywords induction motor; startup transient; brokenrotors; Fast Fourier Transform; Wavelet Transform I. INTRODUCTION Induction machines play a very important role in the field of electromechanical energy conversion. They are widely used in variety of diverse industries product line, process manufacturing, automation applications, heating and air conditioning, transportation, aerospace and marine propulsion application to the health care industry. Even though the induction motors are well designed there may be possibilities of incipient failure which may damages the entire machine operation. Fault diagnosis on induction motor is one of the important concerns in the electric drives industry as 8% of the electric drives industry is working on Induction motors [1]. Severe voltage stresses would be occurred for motors driven by solid state inverters due to rapid switch-on and switch-off of semiconductor switches. Also, induction motors are required to operate in highly corrosive and dusty environments. This results in the development of insulation materials of improved quality and treatment processes. However, the design of cage rotor and its manufacturing have undergone little change. As a result, a large percentage of total induction motor failures is due to rotor problems. Cage rotors are basically of two types: cast and fabricated. Previously, cast rotors were only used in small machines. However, with the advent of cast ducted rotors, casting technology can be used even for rotors of machines in the range of 3 kw. Fabricated rotors are generally found in larger or special-application machines. Cast rotors, although more rugged than the fabricated type, can almost never be repaired once faults like cracked or broken rotor bars develop in them. Broken rotor bars are one of the most severe induction motor faults. It can be caused by thermal stresses due to thermal overload and unbalance, hot spots or excessive losses, sparking (mainly fabricated rotors), magnetic stresses caused by electromagnetic forces, unbalanced magnetic pull, electromagnetic noise and vibration, residual stresses due to manufacturing problems, dynamic stresses arising from shaft torques, centrifugal forces and cyclic stresses, environmental stresses caused by contamination and abrasion of rotor material due to chemicals or moisture, mechanical stresses due to lose laminations, fatigued parts, bearing failure, etc All rights Reserved 496
2 Volume 3, Issue 4, [April 216] ISSN (Online): ; ISSN (Print): Broken rotor bar detection is based on monitoring the amplitudes of the double slip frequency sidebands of the fundamental supply frequency in the current spectrum. It has been shown that the greater is the severity of rotor bar fault, the higher is the amplitude of these sidebands. However, the sideband amplitude is sensitive to motor loading. At no load or at light loads, it is difficult to detect those broken rotor bars due to small rotor currents. Furthermore, a broken rotor bar fault at an early stage or partially broken rotor bars, which can lead to a larger failure or even be catastrophic, may not be detectable even under full load conditions. Therefore, there is a strong need to develop condition monitoring techniques to address these issues to allow earlier detection of rotor faults. During direct-on-line (DOL) starting, the rotor current of the induction machine is very high, typically 5 to 6 times of the rated current. Under these conditions, rotor faults should be much more evident than under normal running conditions. There is also the advantage that the starting current is less sensitive than the running current to the level of motor load, and so valid information can be obtained even with motors with no mechanical load [3]. The fault diagnosis based on the signal processing methods is very widely used as it is a noninvasive method. While using signal processing based fault diagnosis we need to reduce the time taken for the diagnosis. The Fourier Transform produces detailed frequency information of a waveform. It produces excellent results when the waveforms examined are stationary or periodic. Since the signal here used is a transient one, the Fourier transform is an inappropriate one. The advanced version of Fourier Transform is short time Fourier Transform (STFT) which can be used to analyze small sections of the signal at a time. The STFT provides a compromise between time and frequency based views of a signal and it provides some information about both. However, the window size used is of fixed size, the information provided by STFT is of limited precision [4]. The Wavelet Transform (WT) was then introduced with the idea of overcoming the difficulties mentioned above. A windowing technique with variable-size region has been adopted to perform the stator current signal analysis. Wavelet analysis allows to diagnosing the rotor fault even under no load condition. The advantages of using wavelet transform are: it does not suffer from constant window size, it shows variable window for every frequency and does not show resolution problem due to its multi resolution feature. Wavelet analysis allows in providing more precise low frequency information by using long time intervals and high frequency information from short time intervals. Since the variation of stator start up currents shows a clear indication of broken bars, this technique allows an excellent understanding of the nature of the phenomenon. II. BROKEN ROTOR BAR DETECTION TECHNIQUE The induction machine is highly symmetrical. When a three-phase symmetrical stator winding are fed from a symmetrical supply will produce a resultant forward rotating magnetic field at synchronous speed. An exact symmetric field does not produce any backward rotating field. If any asymmetry of the supply or stator winding impedances occurs, it will cause a resultant backward rotating field from the stator winding. Applying the same rotating magnetic field fundamentals to the rotor winding; the main difference from stator winding is that the frequency of the induced electromotive force (EMF) and current in the rotor winding is at slip frequency and not at the supply frequency as in the case of stator. So the rotor currents will also produce a rotating field which will rotate at slip frequency. This happens only when the cage winding is symmetrical. If rotor bars are broken there occurs an asymmetry, then there will be a resultant backward rotating field at slip frequency with respect to the forward rotating rotor. The result of is that, the magnetic field produced by the rotor will oscillate at twice the slip frequency 2sf, where f is the supply frequency. According to Faraday s Law this oscillating magnetic field will induce an electro motive force in the stator winding which will result in the occurrence of stator current components at the same 2sf frequency. However, since the magnetic field produced by these rotor currents are also moving in space at synchronous speed will also induce an EMF and hence current at synchronous frequency. Since All rights Reserved 497
3 Volume 3, Issue 4, [April 216] ISSN (Online): ; ISSN (Print): induction motor, the rotor is moving in a speed less than synchronous speed, the resulting stator EMF and its consequent current will have a component at a frequency equal to (f-2sf) which is equal to f(1-2s). This frequency component in the stator current spectrum is used for rotor fault diagnosis which is called Left Side Band component (LSB). The current component at f(1-2s) produces a consequent torque pulsation at twice slip frequency. This results in consequent speed pulsations at this slip frequency which results in stator current components at f (1+2s). This process of interaction of stator current harmonics, torque oscillation and consequent speed ripple will continue to induce stator current harmonics at (1±ks) f, where k = 2,4,6. III. ROTOR FAULT DIAGNOSIS USING WAVELET TRANSFORM Since non periodic signals have been used to analyze the faulty condition here, Fourier Transform cannot be used as it uses a periodic signal. For getting valid information about a faulty condition, transient signals have been used. If a signal is distorted in a localized time instant, the whole frequency spectrum can be affected. To reduce the effect of non-periodic signals on the Fourier Transform, the short-time Fourier transform (STFT) is used. But it assumes constant window size for all frequencies. The WT is a powerful signal processing tool used fault diagnosis. The WT, like the STFT, allows time localization of dissimilar frequency components of a given signal; however the main difference between these two is that STFT uses constant window size while WT uses a variable one. As a result both frequency and time resolution is fixed for the consequent waveforms but for WT the wavelets that are the analyzing functions will adjust their time width such that narrow will be the high frequency wavelets and broader will be the low frequency wavelets [5]. Wavelet theory has been proved to be a dominant tool for the analysis of transient processes. The wavelet transform provides a time frequency representation of a signal. Discrete Wavelet Transform (DWT) decomposes the current in a set of signals. Each of these signals contains the information of the original signal. The implementation procedure of DWT for an original signal S is explained here. At the initial stage the signal S is divided into two halves of the frequency bandwidth and sent to both high pass filter and low pass filter. The high frequency signals are called detailed signals and low frequency signals are called approximate signals. Then the output of LPF is again divided into half of the frequency bandwidth and sent to the next stage. When the signal is decomposed into a pre-defined level, this procedure stops. According to Nyquist s theorem, the highest frequency that could contain in the signal must be fs/2 Hertz if the original signal S is sampled at fs Hertz. This frequency is considered as the first detail. Hence the frequency band from to fs/2 will be the approximate 1 and f/2 to fs/4 will be in detail 1. Likewise, the frequency band from fs/4 to fs/8 will be in detail 2 and so on. Daubechies-1 has been selected as the mother wavelet in this paper. The advantage of Daubechies is its perfect reconstruction of signal without losing of information. Furthermore, the precision of broken rotor bar diagnosis can be improved by using high order wavelets [6]. Figure 1. Broken rotor bar detection technique IV. SIMULATION RESULTS Wavelet transform has been widely used for rotor fault diagnosis. The simulation results of current, torque and waveforms obtained for induction motor which is being modelled in MATLAB are shown in Fig. 2., Fig. 3. and Fig.4. Induction motor with broken bars has also been modelled All rights Reserved 498
4 Volume 3, Issue 4, [April 216] ISSN (Online): ; ISSN (Print): MATLAB and the torque, current waveforms are obtained. Fig 5, Fig. 6 shows the speed torque waveform for four and eight number of broken rotor bars respectively. From the results it is evident that as the number of broken bars increases the speed torque curve shows more oscillations as explained in the theoretical part. Figure 2. Simulink model of Induction motor with broken rotor bars s ta to r c u rre n t (A ) time(sec) Figure 3. Stator current for healthy induction motor 6 T o r q u e speed Figure 4. Speed Torque waveform for healthy induction motor 4 2 torque speed Figure 5. Speed torque waveforms for eight broken rotor All rights Reserved 499
5 Volume 3, Issue 4, [April 216] ISSN (Online): ; ISSN (Print): stator current (A) time(sec) Figure 6. Stator current for induction motor with four bars broken 15 current(a) time(sec) Figure 7. Stator current for induction motor with eight bars broken Stator current signals are analyzed for both healthy and healthy condition using wavelet transform. The original signal is decomposed at the 4 th level of decomposition. The signal is decomposed into two, one is detailed signal and other one is approximated signal. High frequency information lies in the detailed signals whereas the low frequency information lies in the approximated signals. The mother wavelet used in this work is Daubechies (db1). The advantage of using Daubechies as the mother wavelet is that it is most stable and while reconstruction of the signal it does not lose any information from the wavelet coefficients. The low frequency approximated fourth level signals are used to diagnose the fault. Approximated level 3 and level 4 have been obtained and compared for different number of broken bars. Figure 8. and Figure 9. show the stator current waveforms for four and eight number of broken bars. The current shows more oscillations and the transient nature is decreased as the number of broken bars increases. Fig. 9 and 1 show the approximated level 3 and level 4 normal current waveforms. Also the decomposed signals for different number of broken bars are shown in Figure 1, Figure 11, and Figure 12. From the results obtained it is clear that 4 th level signals provide a better comparison from healthy motor with a faulty one current No: of samples Figure 8. Approximated db1 level 3 decomposed signal of normal current waveform 2 15 current No: of samples Figure 9. Approximated db1 level 4 decomposed signal for normal All rights Reserved 5
6 Volume 3, Issue 4, [April 216] ISSN (Online): ; ISSN (Print): Current No: of samples Figure 1. Approximated level 3 decomposed signals with four broken bars 2 15 current No:of samples Figure 11. Approximated level 4 decomposed signals with four broken bars V. CONCLUSION Thus it can be concluded that wavelet transform can be successfully used to detect the broken rotor bar condition by analyzing the start up transient current signals. Also approximation signals are used for finding low frequency broken rotor fault diagnosis. If the decomposition on current signal is performed then low frequency information can be obtained which contains useful information of the fault. The analysis is performed for different number of broken bars and the waveforms are obtained. Faults under transient condition can be easily found out using this method. Hence the ability of this tool for early fault detection becomes dominant in the area of induction motor rotor fault detection. REFERENCES [1] S.M Shashidhara, Tradeoff Analysis of Wavelet Transform techniques for the Detection of Broken rotor bars in induction, International Journal of Advanced Research in Electrical, Electronics and Instrumentation,vol.3,ISSN , July 213 [2] Neelam Mehala, Ratna Dahiya, Rotor Faults Detection in Induction Motor by Wavelet Analysis, International Journal of Engineering Science and Technology, vol.1 (3),29 [3] Khadim Moin Siddiqui, V.K. Giri, Broken Rotor Bar Fault Detection in Induction Motors using Transient Current Analysis, International Journal Of Electronics and Communication Technology,vol.2. Oct-Dec,211 [4] A.A. Da Silva et al., Rotating machinery monitoring and diagnosis using short-time Fourier transform and wavelet techniques, Proceedings of the 1997 International Conference on Maintenance and Reliability, Knoxville (USA), vol. 1, pp [5] S.M Shashidhara, Tradeoff Analysis of Wavelet Transform techniques for the Detection of Broken rotor bars in induction, International Journal of Advanced Research in Electrical, Electronics and Instrumentation,vol.3,ISSN , July 213 [6] Jawad Faiz, B.M. Ebrahimi, B.Asaie, A Criterion Function for Broken Bar Fault Diagnosis in Induction Motor under load variations using Wavelet Transform, International Conference on Electrical Machines ans All rights Reserved 51
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