Current research in broken rotor bar (BRB) fault detection in induction motors is primarily focused on a high-frequency resolution analysis of the stator current. Compared with a discrete Fourier transformation, the parametric spectrum estimation technique has a higher frequency accuracy and resolution. However, the existing detection methods based on parametric spectrum estimation cannot realize online detection, owing to the large computational cost. To improve the efficiency of BRB fault detection, a new detection method based on the min-norm algorithm and least square estimation is proposed in this paper. First, the stator current is filtered using a band-pass filter and divided into short overlapped data windows. The min-norm algorithm is then applied to determine the frequencies of the fundamental and fault characteristic components with each overlapped data window. Next, based on the frequency values obtained, a model of the fault current signal is constructed. Subsequently, a linear least squares problem solved through singular value decomposition is designed to estimate the amplitudes and phases of the related components. Finally, the proposed method is applied to a simulated current and an actual motor, the results of which indicate that, not only parametric spectrum estimation technique.
Current research in broken rotor bar (BRB) fault detection in induction motors is primarily focused on a high-frequency resolution analysis of the stator current. Compared with a discrete Fourier transformation, the parametric spectrum estimation technique has a higher frequency accuracy and resolution. However, the existing detection methods based on parametric spectrum estimation cannot realize online detection, owing to the large computational cost. To improve the efficiency of BRB fault detection, a new detection method based on the min-norm algorithm and least square estimation is proposed in this paper. First, the stator current is filtered using a band-pass filter and divided into short overlapped data windows. The min-norm algorithm is then applied to determine the frequencies of the fundamental and fault characteristic components with each overlapped data window. Next, based on the frequency values obtained, a model of the fault current signal is constructed. Subsequently, a linear least squares problem solved through singular value decomposition is designed to estimate the amplitudes and phases of the related components. Finally, the proposed method is applied to a simulated current and an actual motor, the results of which indicate that, not only parametric spectrum estimation technique.
1. S Bindu, V V Thomas. Diagnoses of internal faults of three phase squirrel cage induction motor-A review. Proceedings of the IEEE International Conference on Advances in Energy Conversion Technologies, Manipal, India, January 23-25, 2014:48-54.
2. M R Mehrjou, N Mariun, M H Marhaban, et al. Rotor fault condition monitoring techniques for squirrel-cage induction machine-A review. Mechanical Systems and Signal Process, 2011, 25(8):2827-2848.
3. S M A Cruz, A J M Cardoso. Rotor cage fault diagnosis in threephase induction motors by extend Park's vector approach. Electric Machines and Power Systems, 2000, 28(3):289-299.
4. P Puche-Panadero, M Pineda-Sanchez, M Riera-Guasp, et al. Improved resolution of the MCSA method via Hilbert transform, enabling the diagnosis of rotor asymmetries at very low slip. IEEE Transactions on Energy Conversion, 2009, 24(1):52-59.
5. M Drif, A J M Cardoso. The use of the instantaneous reactive power signature analysis for rotor-cage-fault diagnostics in three phase induction motors. IEEE Transactions on Industrial Electronics, 2009, 56(11):4606-4614.
6. J Huang, F L Niu, J Q Yang. Rotor fault diagnosis for induction motors based on the double PQ transformation. Proceedings of the CSEE, 2006, 26(13):135-140. (in Chinese)
7. P P Wang, L P Shi, Y Zhang, et al. Broken rotor bar fault diagnosis of induction motors using a hybrid bare-bones particle swarm optimization algorithm. Proceedings of the CSEE, 2012, 32(30):73-81. (in Chinese)
8. B Q Xu, L L Sun, X Lie, et al. Improvement of the Hilbert method via ESPRIT for detecting rotor fault in induction motors at low slip. IEEE Transactions on Energy Conversion, 2013, 28(1):225-233.
9. A Garcia-Perez, R D J Romero-Troncoso, E Cabal-Yepez, et al. The application of high-resolution spectral analysis for identifying multiple combined faults in induction motors. IEEE Transactions on Industrial Electronics, 2011, 58(5):2002-2010.
10. H K Shahin, H Humberto, A C Gerard. A high-resolution frequency estimation method for three-phase induction machine fault detection. IEEE Transactions on Industrial Electronics, 2007, 54(4):2305-2314.
11. D Czmarena-Martinez, R Osornio-Rios, R J Romero-Troncoso, et al. Fused empirical mode decomposition and MUSIC algorithms for detecting multiple combined faults in induction motors. Journal of Applied Research and Technology, 2015, 13(1):160-167.
12. R J Romero-Troncoso, A Garcia-Perez, D Morinigo-Sotelo, et al. Rotor unbalance and broken rotor bar detection in inverter-fed induction motors at start-up and steady-state regimes by high resolution spectral analysis. Electric Power Systems Research, 2016, 133(1):142-148.
13. B Q Xu, L L Sun, H M Li. A detection method for rotor bar fault in induction motors based on high frequency resolution spectrum estimation technique and optimization algorithm. Proceedings of the CSEE, 2013, 33(3):140-147. (in Chinese)
14. S Pu, C Zheng, V Yuriy, et al. A new diagnosis of broken rotor bar fault extent in three phase squirrel cage induction motor. Mechanical Systems and Signal Processing, 2014, 42(3):388-403.
15. E El, C Vincent, B Mohamed. Induction machine faults detection using stator current parametric spectral estimation. Mechanical Systems and Signal Processing, 2015, 52(53):447-464.
16. D H Hwang, Y W Youn, et al. Robust diagnosis algorithm for identifying broken rotor bar faults in induction motors. Journal of Electrical Engineering and Technology, 2014, 9(1):37-44.
17. B Bessam, A Menacer, M Boumehraz, et al. Detection of broken rotor bar faults in induction motor at low load using neural network. Isa Transactions, 2016, 64(8):241-246.
18. M O Mustafa, D Varagnolo, et al. Detecting broken rotor bars in induction motors with model-based support vector classifiers. Control Engineering Practice, 2016, 52(7):15-23.
19. M O Mustafa, G Nikolakopoulos, T Gustafsson, et al. A fault detection scheme based on minimum identified uncertainty bounds violation for broken rotor bars in induction motors. Control Engineering Practice, 2016, 48(3):63-77.
20. Y Trachi, E Elbouchikhi, V Choqueuse, et al. Induction machines fault detection based on subspace spectral estimation. IEEE Transactions on Industrial Electronics, 2016, 63(9):5641-5651.
21. W J Sun, S Y Shao, R Q Yan. Induction motor fault diagnosis based on deep neural network of sparse auto-encoder. Journal of Mechanical Engineering, 2016, 52(9):65-71. (in Chinese)
22. D Camarena-Martinez, C A Perez-Ramirez, M Valtierra-Rodriguez, et al. Synchrosqueezing transform-based methodology for broken rotor bars detection in induction motors. Measurement, 2016, 90(8):519-525.
23. M Abd-El-Malek, A K Abdelsalam, O E Hassan. Induction motor broken rotor bar fault location detection through envelope analysis of start-up current using Hilbert transform. Mechanical Systems and Signal Processing, 2017, 93(9):332-350.
24. T A Garcia-Calva, D Morinigo-Sotelo, R D Romero-Troncoso. Non-uniform time resampling for diagnosing broken rotor bars in inverter-fed induction motors. IEEE Transactions on Industrial Electronics, 2017, 64(3):2306-2315.
25. R A Lizarraga-Morales, C Rodriguez-Donate, et al. Novel FPGAbased methodology for early broken rotor bar detection and classification through homogeneity estimation. IEEE Transactions on Instrumentation and Measurement, 2017, 66(9):1-10.
26. A Sapena-Bano, J Burriel-Valencia, M Pineda-Sanchez, et al. The harmonic order tracking analysis method for the fault diagnosis in induction motors under time-varying conditions. IEEE Transactions on Energy Conversion, 2017, 32(1):244-256.
27. Z Leonowicz, T Lobos, J Resmez. Advanced spectrum estimation methods for signal analysis in power electronics. IEEE Transactions on Industrial Electronics, 2003, 50(3):1-6.
28. W Yang, L Liu, S D Zhou, et al. Modal parameter identification of time-varying structures via moving least square method. Journal of Mechanical Engineering, 2016, 52(3):79-85. (in Chinese)
29. M Y Zheng, B J Zhang, J Zhang, et al. Physical parameter identification method based on modal analysis for two-axis onroad vehicles:theory and simulation. Chinese Journal of Mechanical Engineering, 2016, 29(4):756-764.
30. F Liu, G H Xu, L Liang, et al. Least squares evaluations for form and profile errors of ellipse using coordinate data. Chinese Journal of Mechanical Engineering, 2016, 29(5):1020-1028.
31. M Labibzadeh. Voronoi based discrete least squares meshless method for heat conduction simulation in highly irregular geometries. Chinese Journal of Mechanical Engineering, 2016, 29(1):98-111.
32. Q J Guo, S Fan, R F Xu, et al. Spindle thermal error optimization modeling of a five-axis machine tool. Chinese Journal of Mechanical Engineering, 2017, 30(3):746-753.
