Application of Notch Filtering under Low Sampling Rate for Broken Rotor Bar Detection with DTFT and AR based Spectrum Methods

Transcription

1 Alication of Notch Filtering under Low Samling Rate for Broken Rotor Bar Detection with DTFT and AR based Sectrum Methods B. Ayhan H. J. Trussell M.-Y. Chow M.-H. Song IEEE Student Member IEEE Fellow IEEE Senior Member IEEE Member Abstract- In this work, we have demonstrated with exerimental results that the use of a lower samling rate with a notch filter is feasible for motor current signature analysis in broken rotor bar detection with DTFT (Discrete Time Fourier Transform) and AR (Auto Regressive) based sectrum methods. The use of the lower samling rate does not affect the erformance of the fault detection, while requiring much less comutation and low-cost in imlementation, which would make it easier to imlement in embedded systems for motor condition monitoring. Index Terms-- Fault Diagnosis, Sectral Analysis, Induction Motors, Broken Rotor Bar, MCSA I. INTRODUCTION Induction motors have dominated in the field of electromechanical energy conversion by having 80% of the motors in use [1]. The alications of induction motors are widesread. The failure of induction motors can result in a total loss of the machine itself, in addition to a likely costly downtime of the whole lant. More imortant, these failures may even result in the loss of lives, which cannot be tolerated. Thus, health monitoring techniques to revent induction motor failures are of great concern in industry and are gaining increasing attention [2]-[3]. Rotor failures are among these failures and they now account for the 5-10% of total induction motor failures [4]. Since 1980, the broken rotor bar fault detection roblem has created substantial interest among researchers. Several monitoring techniques have been develoed, most of which are based on vibration, thermal and motor current signature monitoring (MCSA) [5]. MCSA techniques are gaining more attention because of their easiness to use since they do not require access to the motor [6]. In recent years, several advanced signal rocessing techniques have been alied for motor current signature analysis. Some of these techniques are High Resolution Sectral Analysis, Higher Order Statistics and Wavelet Analysis [1], [6]. The broken rotor bar secific frequencies, which are also called the sideband frequencies, are located around the main line frequency. The difference (in frequency) between the closest sideband and the main line frequency deends on the motor sli factor. Motor sli factor is found using the motor rotor seed where higher sli values indicate higher motor load conditions, and lower sli values corresond to lower load conditions. The difference (in frequency) between the closest sideband and the main line frequency narrows down as the motor goes to a lower load condition. Thus, the frequency resolution must be selected higher than the difference between the closest sideband and the main line frequency; otherwise, the comuted sectrum amlitudes at the sideband frequencies will not be detected, since the resolution would not be adequate enough to show the sidebands. In sectral analysis, in addition to the tye of the windowing function and the length of the window, the samling rate determines the frequency resolution. Thus, the selection of the samling rate is imortant. In revious works regarding the sectrum analysis of broken rotor bar fault, in [7], 2 khz is alied, in [8], 1.5 khz is used, and in [6], 1 khz is alied as the samling rate. However, there is not much discussion secific to the selection of the samling rate. In this work, we have alied a lower samling rate of 200 Hz. One of the reasons that we select 200 Hz is that, the sidebands of interest are in the Hz region, thus higher frequency regions will not rovide any information, and a samling rate of 200 Hz is believed to rovide good erformance without any aliasing effects. With the alied 200 Hz samling rate and different windowing functions used with DTFT, the frequency resolution in this work takes a value between 1-6 Hz where the difference between the closest sideband and the main line frequency is 9.30± 0.77 Hz. Another reason is that, a notch filter, which will not cause any significant suression at the sidebands, can be designed efficiently at a lower samling rate of 200 Hz. In addition to these reasons, from a general oint of view, the use of a lower samling rate results in much less comutation and lowcost in imlementation. Thus, it would be easier to design embedded systems with resect to software and hardware imlementation for motor condition monitoring alications. In this work, the induction motor current data used is collected from an actual exeriment setu in a laboratory environment. The exeriments have been carried out under full load condition of the motor. The healthy and one broken rotor bar motor current data are samled at 10 khz in order to allow a wide range of study with the samling rate. The detection of the faults is done at this rate. Then the data are decimated in order to decrease the original samling rate that is alied in the exeriments to a lower value, 200 Hz, and show that the use of the lower samling rate does not affect the erformance of the fault detection. The nonarametric sectrum method DTFT and the arametric method Yule-AR have been alied with the lower samling rate. Throughout the sectrum 1

2 comutation, only the sectrum amlitudes at the lower and uer sideband broken rotor bar fault secific frequencies are comuted, rather than comuting the overall sectrum. In this way, exact sectrum amlitudes are obtained, which imroves the healthy-faulty discrimination erformance, and decrease the comutational cost considerably. The results indicate that the sidebands can be clearly seen with DTFT, while the sidebands can not be detected with the Yule-AR method. Thus, a second order notch filter is designed to suress the main line frequency and isolate the broken rotor bar secific sideband frequencies for the Yule-AR method. This allows the identification of the characteristic sidebands. The sectrum amlitudes of the healthy and one broken rotor bar motor data resulting from each technique are evaluated using a statistical measure based on a hyothesis test with resect to determining the feature extraction erformance. This aer is organized as follows: Section II discusses the frequencies of interest to detect the broken rotor bar fault. Section III resents the exeriment setu and motor data secifications. The exerimental results and statistical analysis are described in Section IV. Finally, Section V concludes the findings of this work. II. BROKEN ROTOR BAR FREQUENCY SIGNATURES Kliman, Elkasabgy [9]-[10] used motor current signature analysis (MCSA) methods to detect broken rotor bar faults by investigating the sideband comonents around the sulied current fundamental frequency (i.e. the line frequency), f o : f = (1± 2) s f, (1) b where f b are the sideband frequencies associated with the broken rotor bar, s is the er unit motor sli. The sli s is defined as the relative mechanical seed of the motor, n m, with resect to the motor synchronous seed, n s, as: ns nm s =. (2) n The motor synchronous seed, n s, is related to the line frequency f o as: 120 f n o s =, (3) P where P is the number of oles of the motor and constant 120 is used to exress the motor synchronous seed, n, in revolutions er minute (rm) unit. III. EXPERIMENT SETUP AND MOTOR DATA SPECIFICATIONS In order to investigate the feature extraction erformance of the two investigated MCSA techniques for the broken rotor bar detection roblem under a lower samling rate, we erformed exeriments on an actual induction motor. The characteristics of the 3-hase induction motor used in our exeriment are listed in Table I. The motor was tested with a healthy rotor and with a faulty rotor that had one broken rotor bar. The broken rotor bar fault was induced by filling one of the rotor bars full with anchoring cement before the die-casting rocess. Anchoring cement is a high strength, fast-setting s o s gysum cement with low conductivity. The overall data collection scheme and the actual exeriment setu icture are deicted in Fig. 1 and Fig. 2, resectively. Table I. Induction motor characteristics used in the exeriment. Descrition Value Power 0.75 kw (1H) Inut Voltage 380 V Full Load Current 2.2 A Suly Frequency 60 Hz Number of Poles 4 Number of Rotor Slots 44 Number of Stator Slots 36 Full Load Torque 0.43 kg m Full Load Seed 1690 rm The induction motor was fed through a 3 hase ABB, ACS 501 inverter. A Tektronix TM 5003 current amlifier amlifies the induction motor stator currents before being sent to the interfacing Pentium PC through the oscilloscoe. The needed load condition of the induction motor was established by connecting the test motor to a DC Motor, which is used as a generator and is caable of simulating any desired load condition. The seed of the induction motor was measured by a digital stroboscoe. The exeriments involved collecting three hase stator induction motor current and seed data for the full load condition of the motor both with one broken rotor bar fault and without any fault. The motor load condition is determined according to the motor namelate information given in Table I. Thus, there are two different exeriment cases: healthy motor (no broken bar) under full load and motor with one broken rotor bar under full load. For each individual case, 20 sets of motor current data were collected with samling rate 10 khz, Fs=10 khz. Thus, each motor current data set contains 10,000 samles for a duration of one second. IV. EXPERIMENTAL RESULTS AND ANALYSIS As described in Section II, broken rotor bar fault secific frequencies deend on motor s sli, which is a function of motor s synchronous seed and motor s actual seed. In this study, we investigate the sectrum amlitudes of the motor current (hase-a) at the two frequencies secific to broken rotor bar fault. These two frequencies are the first lower and uer sidebands, (1 2) s f o and (1+ 2) s f o, resectively, which are derived from equation (1). Finding the sectrum amlitudes at the actual frequency comonents, f, which are secific to broken b rotor bar fault, is imortant in order to make an accurate decision about the existence of a fault. These frequency comonents are comuted by first incororating the actual motor seed data values into equation (2) to find the sli 2

3 values. The comuted sli values are then used in (1) to find the frequency comonents. According to the exerimental data, motor seed under full load condition varies between 1649 and 1672 rm. Thus, using equation (1), the lower sideband frequency location is found to vary between Hz, while the uer sideband frequency location varies between Hz. The motor current data is decimated with a decimation rate of 50. In this way, the samling rate is reduced by a factor of 50, Fs=200 Hz. Fig. 3 deicts the DTFT sectrums of the decimated healthy and faulty motor current data with Hanning window. The lower and uer sidebands should be examined at Hz and Hz according to the corresonding seed data. In Fig. 3, these frequency locations are marked with vertical lines. The solid line reresents the sectrum of the healthy motor data, and the dashed line corresonds to the sectrum of the broken rotor bar data. From Fig. 3, it can be clearly seen that the sidebands of interest can be detected by the DTFT method with the lower samling rate, Fs=200 Hz. For the feature extraction erformance analysis of the two investigated methods, we will comute the DTFT at only the two sideband frequencies, (1± 2) s f o, which are indicated by the vertical lines in Fig. 3. Fig. 4 deicts the Yule-AR sectrum of the decimated healthy and faulty motor current data with a model order of 30. Unlike the DTFT method, the two sidebands can not be seen, since the dominance of the main line frequency does not allow the sidebands to aear with the Yule-AR method. Thus, a filtering rocess is needed in order to suress the main line frequency. In art A of this section, the filter design rocess is introduced, which will enable the Yule- AR method to be alicable for broken rotor bar detection. A. Notch filter design The motivation behind alying a notch filter is to isolate the two sidebands of interest by suressing the dominance of the main line frequency, such that Yule-AR method can be successfully alied for broken rotor bar detection. At the lower samling rate of Fs=200 Hz, the notch filter can be imlemented effectively for reasonable ole radii, r. We have evaluated the magnitude resonses of several filter designs and considered their transient resonse, when alied to motor current data. The sectrums in Fig. 5 corresond to the same healthy and broken rotor motor current data airs that were used in Fig. 3-Fig. 4. A model order of 30 has been alied in the Yule-AR method. Before alying Yule-AR, the decimated motor current data is filtered with a second order notch filter, having a r value of Fig. 5 verifies that the lower and uer sidebands can be successfully detected after notch filtering with the Yule-AR method with a lower samling rate, Fs=200 Hz. Fig. 6 illustrates that the sidebands can also be seen with the notch filtered data using DTFT (with Hanning window) under Fs=200 Hz. Fig. 1. Motor data collection scheme. Fig. 2. Actual exeriment setu. 3

4 Fig. 3. DTFT of a healthy and faulty motor current data with Hanning window (no filtering alied, Fs = 200 Hz). Fig. 4. Yule-AR sectrum of the decimated data (Fs=200 Hz, model order 30). Fig. 5. Yule-AR sectrum of the notch filtered data with Fs=200 Hz. In order to illustrate that the lower samling rate with a notch filter can be successfully alied for broken rotor bar detection with the two investigated sectrum methods; we have incororated a erformance measure in our analyses. We use t-test -value results to determine if the hyothesis test is significant with the sectrum data under investigation [11]. In general, the t-test allows us to assess whether the means of two grous are statistically different from each other. In our case the two grous under comarison are healthy and faulty sectrum estimates under the full load condition of the motor. The numerical value that the -value yields is a robability value, which gives information on whether the two grous differ from each other and at what degree. As - values get smaller, the discrimination between the two grous becomes more significant. In the remaining arts of this section, we will show the feature extraction erformance of the two investigated techniques. For the DTFT method, we will consider three cases: higher samling rate with no filtering, lower samling rate (after decimation) with no filtering, and lower samling rate with notch filter. For the Yule-AR method, we will consider the lower samling rate with no filtering and lower samling rate with notch filter cases only. This is because Fig. 6. DTFT of the notch filtered data with Fs=200 Hz. with the Yule-AR method the sidebands of interest can not be seen without filtering. Thus, the case of higher samling rate with no filtering is of no use. B. Feature extraction erformance of DTFT In this work, we have considered several windowing techniques when alying the DTFT method since the tye of the windowing technique is a significant factor that affects the feature extraction erformance. We have alied eight different windows with the DTFT method: rectangular, triangular, Hamming, Gaussian, Hanning, Parzen, Nuttall, and Chebyschev (100 db). Table II and Table III deict the -values for the DTFT method with the alied eight windows at the lower and uer sidebands for the three cases. It has to be noted that filtering results in the generation of an unsteady outut data ortion. In the comutation of the sectrum amlitudes, only steady state data ortion is considered, that is, we have avoided using the transient ortions. Among the alied windows, for the higher samling rate with no filtering and lower samling rate with no filtering cases, Hanning, Parzen, Nuttall, and Chebyschev (100dB) windows rovide high healthy-faulty discrimination erformance, while rectangular, triangular, Gaussian, and Hamming windows are not 4

5 satisfactory. This is caused by the leakage of the main line frequency. From Fig. 3, it can be seen that the db magnitude of the main line frequency ( f o =60 Hz) signal, is about 50 db above the sideband signals. In order to avoid leakage effects on the sidebands, the main line frequency needs to be suressed more than 50 db. The rectangular, triangular, Hamming and Gaussian windows barely suress the main line frequency, and are not adequate for suression of 50 db and above. On the other hand, Hanning, Parzen, Nuttall, and Chebyschev (100dB) rovide adequate suression. A second way to suress the main line frequency is with a notch filter. For the lower samling rate with notch filter case, all windows rovide satisfactory results, as exected. It is also observed that the Hamming window generates considerably lower -values after notch filtering, and the uer sideband, (1+ 2) s f o, has more discriminative information when comared to the lower sideband, (1 2) s f o. In order to give a visual insight to the reader about the relation between the - value and the discrimination rate, Fig. 7 and Fig. 8 deict the DTFT amlitudes of the notch filtered healthy and faulty data sets with Fs=200 Hz for the lower and uer sidebands, resectively. In Fig. 7 and Fig. 8, the alied second order notch filter has a r value of 0.91, and the DTFT amlitudes are comuted with a Hamming window that has a window size of 150. C. Feature extraction erformance of Yule-AR Table IV deicts the -values with resect to the Yule- AR method at the lower and uer sidebands for the lower samling rate with no filtering case. From Table IV, we can see that no useful classification is ossible without suressing the dominant main line frequency. Table V deicts the -values with resect to the Yule- AR method at the lower sideband for the lower samling rate with notch filtering case. In Table V, -values corresond to different combinations of model orders and ole radius of notch filters. It is observed that a -value as low as 3.26e-9 is obtained with r equal to 0.91 and model order 30. Similarly, Table VI deicts the -values with resect to the Yule-AR method at the uer sideband for the lower samling rate with notch filtering case. It is examined that a -value of 5.70e-22 has been obtained with r equal to 0.85 and model order 90. The X in Table V and Table VI indicate that the Yule-AR sectrum can not be comuted since the data length is smaller than the alied model order for these cases. With resect to Table V and Table VI, as the oles of the notch filter get closer to the unit circle, the imulse resonse of the filter gets longer and the length of the usable data becomes smaller. Thus, we get no results or oor -values for these cases. Before alying filtering, Yule-AR results are useless because of not roviding any information in terms of healthyfaulty discrimination, as can be seen from the -values deicted in Table IV. After filtering, the dominance of the main line frequency is suressed, and the sidebands are significantly isolated. The decimation results indicate that a lower initial samling rate can be used for broken rotor bar fault detection with DTFT method. It is the alied window function that makes a dee imact on the feature extraction erformance with the nonarametric sectrum methods. For examle, alying a rectangular window without notch filtering of the motor current data generates misleading results in the DTFT method. There is no need for filtering with the DTFT method if a window function that can significantly reduce the sectral leakage effects is alied. Table II. -values for DTFT with different windows for the lower sideband for the three cases. Window tye (1 2) s f o no filtering (1+ 2) s f o no filtering (1+ 2) s f o notch filtering winsize:10,000,fs:10kh winsize:200, Fs=200 Hz winsize:150, r =0.91,Fs=200 Hz z Rectangular e-007 Triangular e-008 Hamming e-008 Gaussian e-008 Hanning e e e-008 Parzen e e e-007 Nuttall e e e-007 Chebyschev (100 db) e e e-007 Table III. -values for DTFT with different windows for the uer sideband for the three cases. Window tye (1+ 2) s f o no filtering (1+ 2) s f o no filtering (1+ 2) s f o notch filtering winsize:10,000,fs:10kh winsize:200, Fs=200 Hz winsize:150, r =0.91, Fs=200 Hz z Rectangular e-018 Triangular e-024 Hamming e-024 5

6 Gaussian e e e-024 Hanning e e e-023 Parzen e e e-022 Nuttall e e e-021 Chebyschev (100 db) e e e-022 Fig. 7. DTFT amlitudes of the notch filtered healthy and faulty data sets at the lower sideband. Fig. 8. DTFT amlitudes of the notch filtered healthy and faulty data sets at the uer sideband. Table IV. -values with resect to Yule-AR for the lower and uer sidebands (no filtering, Fs=200 Hz). Model order Sideband (1 2) s f o (1+ 2) s f o Table V. -values with resect to Yule-AR for the lower sideband (notch filtering, Fs=200 Hz). Filter Model order r e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e X X X X X Table VI. -values with resect to Yule-AR for the uer sideband (notch filtering, Fs=200 Hz). Filter Model order r e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e-11 X e e e X X X X In order to aly the Yule-AR method for broken rotor bar detection, the dominance of the main line frequency must be suressed. Otherwise, the sidebands of interest, can not be extracted even high model orders are used. The suression of the main line frequency and isolation of the sidebands can be done by alying a second order notch filter. After notch filtering, Yule-AR can be alied successfully, and rovide accurate healthy-faulty discrimination as the DTFT method. V. CONCLUSION This aer has illustrated with exerimental results that a lower samling rate with a notch filter can be successfully alied with the DTFT and AR based sectrum methods for the broken bar detection roblem and the use of the lower samling rate does not affect the erformance of the fault 6

7 detection. The utilization of a lower samling rate is significantly imortant because lower samling rate means less comutation and low-cost in imlementation which could lead to more effective and less costly embedded system designs for motor condition monitoring alications. REFERENCES [1] M.E.H. Benbouzid, G.B. Kliman, What stator current rocessing based technique to use for induction motor rotor faults diagnosis?, IEEE Power Engineering Review, August [2] B. Li, M.-Y. Chow, Y. Tisuwan, J.C. Hung, Neural-networkbased motor rolling bearing fault diagnosis, IEEE Trans. Industrial Electronics on, vol. 47, no. 5, Oct. 2000, [3] S. Altug, M.-Y. Chow, H.J. Trussell, Fuzzy inference systems imlemented on neural architectures for motor fault detection and diagnosis, IEEE Trans. on Industrial Electronics, vol. 46, no. 6, Dec. 1999, [4] M. Haji, H. A. Toliyat, Pattern recognition a technique for induction machines rotor broken bar detection, IEEE Trans. on Energy Conversion, vol. 16, no. 4, Dec. 2001, [5] G. B. Kliman, J. Stein, Methods of motor current signature analysis, Electric machines and ower systems, , [6] M.E.H. Benbouzid, M. Vieira, C. Theys, Induction motors faults detection and localization using stator current advanced signal rocessing techniques, IEEE Trans. on Power Electronics, vol. 14, no. 1, Jan. 1999, [7] G. Didier, and et al., Rotor cage fault detection in induction motor using global modulation index on the instanteneous ower sectrum, SDEMPED 2003, Atlanta, GA, USA, August [8] F. Cuertino et al., Comarison of sectral estimation techniques alied to induction motor broken bars detection, SDEMPED 2003, Atlanta, GA, USA, August [9] G. B. Kliman et al., Non-invasive detection of broken rotor bars in oerating induction motors, IEEE Trans. on Energy Conversion vol. EC-3, no. 4, , [10] N.M. Elkasabgy, A. R. Eastham, G. E. Dawson, Detection of broken bars in the cage rotor on an induction machine, IEEE Trans. on Industrial Alications, vol. IA-22, no. 6, , Jan/Feb [11] D.C. Montgomery, G.C. Runger, N.F. Hubele, Engineering Statistics, John Wiley & Sons, New York,