Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks

IEEE Transactions on Industrial Electronics

Volumn 63, Issue 11, 2016, Pages 7067-7075

  Ince, Turker ^a   Kiranyaz, Serkan ^b   Eren, Levent ^a   Askar, Murat ^a   Gabbouj, Moncef ^c  

^a IZMIR UNIVERSITY OF ECONOMICS   (Turkey)

^b Qatar University   (Qatar)

^c TAMPERE UNIVERSITY OF TECHNOLOGY   (Finland)

Author keywords

Convolutional neural networks (CNNs); motor current signature analysis (MCSA)

Indexed keywords

CLASSIFICATION (OF INFORMATION); CONDITION MONITORING; CONVOLUTION; EXTRACTION; FEATURE EXTRACTION; NEURAL NETWORKS;

ADAPTIVE DESIGNS; COMPUTATIONAL COSTS; CONVOLUTIONAL NEURAL NETWORK; FEATURE EXTRACTION ALGORITHMS; FEATURE EXTRACTION AND CLASSIFICATION; MOTOR CURRENT SIGNATURE ANALYSIS; REAL-TIME APPLICATION; SUB-OPTIMAL CHOICES;

FAULT DETECTION;

EID: 84994474581     PISSN: 02780046     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIE.2016.2582729     Document Type: Article

References (34)

1. A. Giantomassi, "Electric motor fault detection and diagnosis by kernel density estimation and Kullback-Leibler divergence based on stator current measurements, " IEEE Trans. Ind. Electron., vol. 62, no. 3, pp. 1770-1780, Mar. 2015.
2. Z. Gao, C. Cecati, and S. X. Ding, "A survey of fault diagnosis and faulttolerant techniques - Part I: Fault diagnosis with model-based and signalbased approaches, " IEEE Trans. Ind. Electron., vol. 62, no. 6, pp. 3757-3767, Jun. 2015.
3. X. Dai and Z. Gao, "From model, signal to knowledge: A data-driven perspective of fault detection and diagnosis, " IEEE Trans. Ind. Informat., vol. 9, no. 4, pp. 2226-2238, Apr. 2013.
4. 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 Proc. IEEEWorkshop Electr.Mach. Des. Control Diagn., Paris, France, Mar. 2013, pp. 196-209.
5. W. Zhou, T. Habetler, and R. Harley, "Bearing fault detection via stator current noise cancellation and statistical control, " IEEE Trans. Ind. Electron., vol. 55, no. 12, pp. 4260-4269, Dec. 2008.
6. A. Bellini, F. Filippetti, C. Tassoni, and G. A. Capolino, "Advances in diagnostic techniques for inductionmachines, " IEEE Trans. Ind. Electron., vol. 55, no. 12, pp. 4109-4125, Dec. 2008.
7. C. Kral, T. G. Habetler, and R. G. Harley, "Detection of mechanical imbalances of induction machines without spectral analysis of time domain signals" IEEE Trans. Ind. Appl., vol. 40, no. 4, pp. 1101-1106, Mar./Apr. 2004.
8. R. R. Schoen, T. G. Habetler, F. Kamran, and R. G. Bartheld, "Motor bearing damage detection using stator current monitoring, " IEEE Trans. Ind. Appl., vol. 31, no. 6, pp. 1274-1279, Dec. 1995.
9. G. B. Kliman, W. J. Premerlani, B. Yazici, R. A. Koegl, and J. Mazereeuw, "Sensorless online motor diagnostics, " IEEE Comput. Appl. Power, vol. 10, no. 2, pp. 39-43, Feb. 1997.
10. 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 Trans. Ind. Electron., vol. 62, no. 3, pp. 1791-1802, Mar. 2015.
11. D. Z. Li, W. Wang, and F. Ismail, "An enhanced bispectrum technique with auxiliary frequency injection for induction motor health condition monitoring, " IEEE Trans. Instrum. Meas., vol. 67, no. 10, pp. 2279-87, Oct. 2015.
12. L. Eren andM. J. Devaney, "Bearing damage detection via wavelet packet decomposition of the stator current, " IEEE Trans. Instrum. Meas., vol. 53, no. 2, pp. 431-436, Feb. 2004.
13. L. Eren, A. Karahoca, and M. J. Devaney, "Neural network based motor bearing fault detection, " in Proc. Instrum. Meas. Technol. Conf., May 2004, pp. 1657-1660.
14. Z. Ye, B. Wu, and A. R. Sadeghian, "Current signature analysis of induction motor mechanical faults by wavelet packet decomposition, " IEEE Trans. Ind. Electron., vol. 50, no. 6, pp. 1217-28, Jun. 2003.
15. J. Liu, W. Wang, F. Golnaraghi, and K. Liu, "Wavelet spectrum analysis for bearing fault diagnostics, " Meas. Sci. Technol., vol. 19, no. 1, pp. 1-10, Jan. 2008.
16. R. Yan, R. X. Gao, and X. Chen, "Wavelets for fault diagnosis of rotary machines: A review with applications, " Signal Process., vol. 96 Part A, pp. 1-15, Mar. 2014.
17. R. Di Stefano, S. Meo, and M. Scarano, "Induction motor fault diagnostic via artificial neural network, " in Proc. IEEE Int. Symp. Ind. Electron., Santiago, Chile, May 1994, pp. 220-225.
18. B. Li, M.-Y. Chow, Y. Tipsuwan, and J. C. Hung, "Neural network based motor rolling bearing fault diagnosis, " IEEE Trans. Ind. Electron., vol. 47, no. 5, pp. 1060-1069, Oct. 2000.
19. 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, " Mech. Syst. Signal Process., vol. 27, pp. 696-711, Feb. 2012.
20. X. Li, A. Zhang, 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, Aug. 2013.
21. K. Kim, A. G. Parlos, and R. M. Bharadwaj, "Sensorless fault diagnosis of induction motors, " IEEE Trans. Ind. Electron., vol. 50, no. 5, pp. 1038-1051, Oct. 2003.
22. C. T. Kowalski and T. O-Kowalska, "Neural network application for induction motor faults diagnosis, " Math. Comput. Simul., vol. 63, nos. 3-5, pp. 435-448, Nov. 2003.
23. V. T. Tung, B.-S. Yang, M.-S. Oh, and A. C. C. Tan, "Fault diagnosis of inductionmotor based on decision trees and adaptive neurofuzzy inference, " Expert Syst. Appl., vol. 36, no. 2, pp. 1840-1849, Feb. 2009.
24. W.-Y. Chen, J.-X. Xu, and S. K. Panda, "Application of artificial intelligence techniques to the study of machine signatures, " in Proc. IEEE 20th Int. Conf. Electr. Mach., Marseille, France, Sep. 2012, pp. 2390-2396.
25. 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 Trans. Ind. Electron., vol. 54, no. 1, pp. 250-258, Jan. 2007.
26. F. Zidani, D. Diallo, M. E. H. Benbouzid, and R. Nait-Said, "A fuzzy based approach for the diagnosis of fault modes in a voltage-fed PWM inverter induction motor drive, " IEEE Trans. Ind. Electron., vol. 55, no. 2, pp. 586-593, Feb. 2008.
27. K. Kim and A. G. Parlos, "Induction motor fault diagnosis based on neuropredictors and wavelet signal processing, " IEEE/ASME Trans. Mechatronics, vol. 7, no. 2, pp. 201-219, Jun. 2002.
28. D. H. Wiesel and T. N. Hubel, "Receptive fields of single neurones in the cat's striate cortex, " J. Physiol., vol. 148, pp. 574-591, Oct. 1959.
29. D. C. Ciresan, U. Meier, L. M. Gambardella, and J. Schmidhuber, "Deep big simple neural nets for handwritten digit recognition, " Neural Comput., vol. 22, no. 12, pp. 3207-3220, Dec. 2010.
30. D. Scherer, A. Muller, and S. Behnke, "Evaluation of pooling operations in convolutional architectures for object recognition, " in Proc. Int. Conf. Artif. Neural Netw., Thessaloniki, Greece, Sep. 2010, pp. 92-101.
31. A. Krizhevsky, I. Sutskever, and G. Hinton, "Imagenet classification with deep convolutional neural networks, " in Proc. Adv. Neural Inf. Process. Syst. Conf., Lake Tahoe, Nevada, Dec. 2012, pp. 1097-1105.
32. S. Kiranyaz, T. Ince, and M. Gabbouj, "Real-time patient-specific ECG classification by 1D convolutional neural networks, " IEEE Trans. Biomed. Eng., vol. 63, no. 3, pp. 664-675, Aug. 2015, doi: 10.1109/TBME.2015.2468589.
33. C. Farabet, C. Poulet, J. Han, and Y. LeCun, "CNP: An FPGAbased processor for convolutional networks, " in Proc. IEEE Int. Conf. Field Programmable Logic Appl., Prague, Czech Republic, Sep. 2009, pp. 32-37.
34. V. Wowk, Machinery Vibration, Measurement and Analysis. New York, NY, USA: McGraw-Hill, Jul. 1991.