Bearing fault detection by one-dimensional convolutional neural networks

Mathematical Problems in Engineering

Volumn 2017, Issue , 2017, Pages

  Eren, Levent ^a  

^a IZMIR UNIVERSITY OF ECONOMICS   (Turkey)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); COMPLEX NETWORKS; COMPUTATIONAL COMPLEXITY; CONVOLUTION; DATA MINING; DECISION SUPPORT SYSTEMS; EXTRACTION; FEATURE EXTRACTION; NEURAL NETWORKS; ONE DIMENSIONAL; VIBRATION ANALYSIS;

BEARING FAULT DETECTION; CLASSIFICATION PERFORMANCE; COMPUTATIONAL COSTS; CONVOLUTIONAL NEURAL NETWORK; DETECTION ACCURACY; FEATURE EXTRACTION ALGORITHMS; FEATURE EXTRACTION AND CLASSIFICATION; REAL-TIME APPLICATION;

FAULT DETECTION;

EID: 85027323447     PISSN: 1024123X     EISSN: 15635147     Source Type: Journal    
DOI: 10.1155/2017/8617315     Document Type: Article

References (38)

1. H. A. Toliyat, S. Nandi, S. Choi, and H. Meshgin-Kelk, Electric Machines:Modeling, ConditionMonitoring, and Fault Diagnosis, CRC Press, 2012.
2. R. B. Randall,Vibration-Based ConditionMonitoring: Industrial, Aerospace and Automotive Applications, John Wiley & Sons, Chichester, UK, 2011.
3. H. Henao, G.-A. Capolino, M. Fernandez-Cabanas et al., "Trends in fault diagnosis for electrical machines: a review of diagnostic techniques," IEEE Industrial Electronics Magazine, vol. 8, no. 2, pp. 31-42, 2014.
4. W. Zhou, T. G. Habetler, and R. G. Harley, "Bearing fault detection via stator current noise cancellation and statistical control," IEEE Transactions on Industrial Electronics, vol. 55, no. 12, pp. 4260-4269, 2008.
5. P. D. McFadden and J. D. Smith, "Model for the vibration produced by a single point defect in a rolling element bearing," Journal of Sound and Vibration, vol. 96, no. 1, pp. 69-82, 1984.
6. Y.-T. Su and S.-J. Lin, "On initial fault detection of a tapered roller bearing: Frequency domain analysis," Journal of Sound and Vibration, vol. 155, no. 1, pp. 75-84, 1992.
7. R. R. Schoen, T. G. Habetler, F. Kamran, and R. G. Bartheld, "Motor bearing damage detection using stator current monitoring," IEEE Transactions on Industry Applications, vol. 31, no. 6, pp. 1274-1279, 1995.
8. G. B. Kliman, W. J. Premerlani, B. Yazici, R. A. Koegl, and J. Mazereeuw, "Sensorless, online motor diagnostics," IEEE Computer Applications in Power, vol. 10, no. 2, pp. 39-43, 1997.
9. J. Pons-Llinares, J. A. Antonino-Daviu, M. Riera-Guasp, S. B. Lee, T.-J. Kang, and C. Yang, "Advanced induction motor rotor fault diagnosis via continuous and discrete time-frequency tools," IEEE Transactions on Industrial Electronics, vol. 62, no. 3, pp. 1791-1802, 2015.
10. N. Arthur and J. Penman, "Induction machine condition monitoring with higher order spectra," IEEE Transactions on Industrial Electronics, vol. 47, no. 5, pp. 1031-1041, 2000.
11. M. E. H. Benbouzid, M. Vieira, and C. Theys, "Induction motors' faults detection and localization using stator current advanced signal processing techniques," IEEE Transactions on Power Electronics, vol. 14, no. 1, pp. 14-22, 1999.
12. D. Z. Li, W. Wang, and F. Ismail, "An enhanced bispectrum technique with auxiliary frequency injection for induction motor health condition monitoring," IEEE Transactions on Instrumentation and Measurement, vol. 64, no. 10, pp. 2679-2687, 2015.
13. L. Eren and M. J. Devaney, "Bearing damage detection via wavelet packet decomposition of the stator current," IEEE Transactions on Instrumentation and Measurement, vol. 53, no. 2, pp. 431-436, 2004.
14. Z. Ye, B. Wu, and A. Sadeghian, "Current signature analysis of inductionmotor mechanical faults by wavelet packet decomposition," IEEE Transactions on Industrial Electronics, vol. 50, no. 6, pp. 1217-1228, 2003.
15. R. Yan, R. X. Gao, and X.Chen, "Wavelets for fault diagnosis of rotary machines: a review with applications," Signal Processing, vol. 96, pp. 1-15, 2014.
16. S. Prabhakar, A. R. Mohanty, and A. S. Sekhar, "Application of discrete wavelet transform for detection of ball bearing race faults," Tribology International, vol. 35, no. 12, pp. 793-800, 2002.
17. F. Filippetti, A. Bellini, and G.-A. Capolino, "Condition monitoring and diagnosis of rotor faults in induction machines: State of art and future perspectives," in Proceedings of the 1st Workshop on ElectricalMachines Design, Control and Diagnosis, WEMDCD 2013, pp. 196-209, Paris, France, March 2013.
18. R. Di Stefano, S. Meo, and M. Scarano, "Induction motor faults diagnostic via artificial neural network (ANN)," in Proceedings of the IEEE International Symposium on Industrial Electronics (ISIE' 94), pp. 220-225, IEEE, Santiago, Chile, May 1994.
19. L. Eren, A. Karahoca, andM. J. Devaney, "Neural networkbased motor bearing fault detection," in Proceedings of the 21st IEEE Instrumentation and Measurement Technology Conference, pp. 1657-1660, May 2004.
20. B. Li, M.-Y. Chow, Y. Tipsuwan, and J. C. Hung, "Neuralnetwork-based motor rolling bearing fault diagnosis," IEEE Transactions on Industrial Electronics, vol. 47, no. 5, pp. 1060-1069, 2000.
21. G. F. Bin, J. J. Gao, X. J. Li, and B. S. Dhillon, "Early fault diagnosis of rotating machinery based on wavelet packets-empirical mode decomposition feature extraction and neural network," Mechanical Systems and Signal Processing, vol. 27, no. 1, pp. 696-711, 2012.
22. X. Li, A. Zheng, X. Zhang, C. Li, and L. Zhang, "Rolling element bearing fault detection using support vector machine with improved ant colony optimization," Measurement, vol. 46, no. 8, pp. 2726-2734, 2013.
23. K. Kim, A. G. Parlos, and R. M. Bharadwaj, "Sensorless fault diagnosis of induction motors," IEEE Transactions on Industrial Electronics, vol. 50, no. 5, pp. 1038-1051, 2003.
24. C. T. Kowalski and T. Orlowska-Kowalska, "Neural networks application for induction motor faults diagnosis," Mathematics and Computers in Simulation, vol. 63, no. 3-5, pp. 435-448, 2003.
25. V. T. Tran, B.-S. Yang, M.-S. Oh, and A. C. C. Tan, "Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference," Expert Systems with Applications, vol. 36, no. 2, pp. 1840-1849, 2009.
26. W.-Y. Chen, J.-X. Xu, and S. K. Panda, "Application of artificial intelligence techniques to the study of machine signatures," in Proceedings of the 20th International Conference on Electrical Machines (ICEM '12), pp. 2390-2396, Marseille, France, September 2012.
27. M. S. Ballal, Z. J. Khan, H. M. Suryawanshi, and R. L. Sonolikar, "Adaptive neural fuzzy inference system for the detection of inter-turn insulation and bearing wear faults in induction motor," IEEE Transactions on Industrial Electronics, vol. 54, no. 1, pp. 250-258, 2007.
28. F. Zidani, D. Diallo,M. Benbouzid, and R. Nat-Sad, "A fuzzybased approach for the diagnosis of fault modes in a voltagefed PWM inverter induction motor drive," IEEE Transactions on Industrial Electronics, vol. 55, no. 2, pp. 586-593, 2008.
29. K.Kim and A. G. Parlos, "Inductionmotor fault diagnosis based on neuropredictors and wavelet signal processing," IEEE/ASME Transactions on Mechatronics, vol. 7, no. 2, pp. 201-219, 2002.
30. D. Bayram and S. S?eker, "Wavelet basedNeuro-Detector for low frequencies of vibration signals in electric motors," Applied Soft Computing Journal, vol. 13, no. 5, pp. 2683-2691, 2013.
31. H. Liu, X. Wang, and C. Lu, "Rolling bearing fault diagnosis under variable conditions using Hilbert-Huang transform and singular value decomposition," Mathematical Problems in Engineering, vol. 2014, Article ID 765621, 10 pages, 2014.
32. S. Kiranyaz, T. Ince, and M. Gabbouj, "Real-time patientspecific ECG classification by 1-D convolutional neural networks," IEEE Transactions on Biomedical Engineering, vol. 63, no. 3, pp. 664-675, 2016.
33. T. Ince, S. Kiranyaz, L. Eren, M. Askar, and M. Gabbouj, "Real-time motor fault detection by 1-D convolutional neural networks," IEEE Transactions on Industrial Electronics, vol. 63, no. 11, pp. 7067-7075, 2016.
34. STI Field Application Note, Rolling Element Bearings, REB, Sales Technology, League City, Tex, USA, 2012.
35. V. Wowk, Machinery Vibration, Measurement and Analysis, McGraw-Hill, 1991.
36. J. Lee, H. Qiu, G. Yu, and J. Lin, Rexnord Technical Services: Bearing Data Set, 2007.
37. H. Qiu, J. Lee, J. Lin, and G. Yu, "Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics," Journal of Sound and Vibration, vol. 289, no. 4-5, pp. 1066-1090, 2006.
38. C. Farabet, C. Poulet, J. Y. Han, and Y. LeCun, "CNP: an FPGAbased processor for Convolutional Networks," in Proceedings of the International Conference on Field Programmable Logic and Applications, pp. 32-37, Prague, Czech, September 2009.