Detection of Bearing Faults in Induction Motor by a Combined Approach SVD-Kalman Filter
This paper presents a study on the bearing faults detection of the induction motor by a new parametric approach using the stator current signal. This technique is based on two estimators. The first extracts the faults frequencies by the singular value decomposition of the covariance matrix of the stator phase current, the second is the Kalman filter; it estimates the extent of the faults. Indeed, the main advantage of this approach is its very good frequency resolution for a very short acquisition time, something impossible to achieve with the conventional method. Moreover, in order to reduce the important computation time performed by this approach, the proposed solution consists in applying this approach only on the frequency band where the fault signature is likely to appear. Experimental results show the effectiveness of the RM method to incipient bearing fault detection.
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A. H. Boudinar, N. Benouzza, A. Bendiabdellah, and M. E. A. Khodja, Induction Motor Bearing Fault Analysis Using a Root-MUSIC Method, IEEE Transactions on Industry Applications, Vol. 52 (Issue 5): 3851-3860, September-October 2016.
R. A. Guyer, Rolling Bearings Handbook and Troubleshooting Guide (Chilton Book Compagny, Radnor, 1996).
R. R. Schoen, T. G. Habetler, F. Kamran, and R. G. Bartheld, Motor Bearing Damage Detection Using Stator Current Monitoring, IEEE Transaction on Industry Applications, Vol. 31 (Issue 6): 1274–1279, 1995.
B. Trajin, J. Regnier, and J. Faucher, Comparison Between Vibration And Stator Current Analysis for The Detection of Bearing Faults in Asynchronous Drives, IET Electric. Power Application, Vol. 4 (Issue 2): 90–100, 2010.
T. Yang, H. Pen, Z. Wang, and C. S. Chang, Feature Knowledge Based Fault Detection of Induction Motors Through the Analysis of Stator Current Data, IEEE Transactions on Instrumentation and Measurement, Vol. 65 (Issue 3): 549-558, March 2016.
A. Ibrahim, F. Bonnardot, M.E. Badaoui and F. Guillet, Detection of Bearing Damage Using Stator Current, and Voltage to Cancel Electrical Noise, EURASIP Journal on Advances in Signal Processing, Vol. 2011 (Issue 2011) 1–14, 2011.
A. Bellini, A. Yazidi, F. Filippetti, C. Rossi, and G. Capolino, High Frequency Resolution Techniques for Rotor Fault Detection of Induction Machines, IEEE Transaction Industrial Electronics, Vol. 55, (Issue 12): 4200–4209, December 2008.
M. R. Mehrjou,N. Mariun , M.H. Marhaban, and N. Misron, Rotor Fault Condition Monitoring Techniques for Squirrel-Cage Induction Machine A Review, Mechanical Systems and Signal Processing, Vol. 25 (Issue 8):2827–2848 ,2011.
S. Rajagopalan, J. A. Restrepo, J. M. Aller,T. G. Habetler, and R. G. Harley, Nonstationary Motor Fault Detection Using Recent Quadratic Time–Frequency Representations, IEEE Transactions on Industry Applications, Vol. 44 (Issue 3):735 – 744, May-june 2008.
M. Sahraoui, A. J. Marques Cardoso, and A. Ghoggal, The Use of a Modified Prony’s Method to Track the Broken Rotor Bars Characteristic Frequencies and Amplitudes, in Three-Phase Induction Motors , IEEE Transactions on Industry Applications, Vol. 51, (Issue 3): 2136-2147, May-June 2015.
B. Xu, L. Sun, L. Xu, and G. Xu, Improvement Of The Hilbert Method Via Esprit Detecting Rotor Fault in Induction Motors at Low Slip, IEEE Transactions on Energy Conversion., Vol. 28, (Issue 1): 225–233, March 2013.
S. H. Kia, H. Henao, and G.A Capolino, A High-Resolution Frequency Estimation Method for Three Phase Induction Machine Fault Detection, IEEE Transaction Industrial Electronics, Vol. 54, (Issue 4): 2305–2314, August 2007.
G. S. Maruthi, and V. Hegde, Application of MEMS Accelerometer for Detection and Diagnosis of Multiple Faults in the Roller Element Bearings of Three Phase Induction Motor, IEEE Sensors Journal, Vol. 16(Issue 1): 145-152, January 2016.
L. Frosini, and E. Bassi, Stator Current and Motor Efficiency as Indicators for Different Types of Bearing Faults in Induction Motors, IEEE Transaction Industrial Electronics, Vol. 57 (Issue 1): pp. 244–251, January 2010.
A. A. Abdelsalam , A. A. Eldesouky, and A. A. Sallam, Classification of Power System Disturbances Using Linear Kalman Filter and Fuzzy-Expert System, International Journal of Electrical Power & Energy Systems, Vol. 43 (Issue 1): 688-695, December 2012.
K. Neslihan , S. Özgül , and L. Kemal, Interharmonics Analysis of Power Signals With Fundamental Frequency Deviation Using Kalman Filtering, Electric Power Systems Research, Vol. 80 (Issue 9): 1145-1153, September 2010.
El Houssin El Bouchikhi,V. Choqueusea, and M.E.H Benbouzid, Induction Machine Faults Detection Using Stator Current Parametric Spectral Estimation, Journal of Mechanical Systems and Signal Processing, Vol. 52-53, 447-464, February 2015.
S. Chakkor, M. Baghouri , and A. Hajraoui, Performance Analysis of Faults Detection in Wind Turbine Generator Based on High-Resolution Frequency Estimation Methods, International Journal of Advanced Computer Science and Applications, Vol. 5 (Issue 4):139-148, 2014.
Y. J. Li, B. J. Yang, Y. Li, Q. Zeng, Combining Svd with Wavelet Transform in Synthetic Seismic Signal Denoising, Wavelet Analysis and Pattern Recognition Conference, pp. 1831–1836, Beijing, China, November 2007.
F. Auger, M. Hilairet, J. M.Guerrero, E. Monmasson, T. Orlowska-Kowalska, and S. Katsura, Industrial Applications of the Kalman Filter A Review, IEEE Transactions on industrial electronics, Vol. 60 (Issue 12): 5458 – 5471, December 2013.
C. K. Chui et G. Chen, Kalman Filtering with Real-Time Applications (Springer, Berlin, 2009).
Molina-Cabrera, A., Rios, M., A Kalman Latency Compensation Strategy for Model Predictive Control to Damp Inter-Area Oscillations in Delayed Power Systems, (2016) International Review of Electrical Engineering (IREE), 11 (3), pp. 296-304.
Ghadhban, H., Muniyandi, R., Improved Kalman Filter Based LAR in Vehicular Ad Hoc Network, (2016) International Review on Modelling and Simulations (IREMOS), 9 (5), pp. 361-366.
Grillo, C., Montano, F., An EKF Based Method for Path Following in Turbulent Air, (2017) International Review of Aerospace Engineering (IREASE), 10 (1), pp. 1-6.
Al-Sbou, Y., Optimizing Image Compression Using Singular Value Decomposition Based on Structural Similarity Index, (2017) International Journal on Communications Antenna and Propagation (IRECAP), 7 (4), pp. 316-325.
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