Accurate bearing faults classification based on statistical-time features, curvilinear component analysis and neural networks

IECON Proceedings (Industrial Electronics Conference)

Volumn , Issue , 2012, Pages 3854-3861

  Delgado, M ^a   Cirrincione, G ^b   Garcia, A ^a   Ortega, J A ^a   Henao, H ^b  

^a UNIVERSITAT POLITÈCNICA DE CATALUNYA   (Spain)

^b UNIVERSITÉ DE PICARDIE JULES VERNE   (France)

Author keywords

Bearing balls; Classification algorithms; Fault detection; Feature extraction; Neural networks; Time domain analysis; Vibrations

Indexed keywords

BEARING BALL; BEARING FAULT; CLASSIFICATION ALGORITHM; CURVILINEAR COMPONENT ANALYSIS; ELECTRICAL MACHINE; HIERARCHICAL NEURAL NETWORKS; LOCALIZED FAULT; OPERATION CONDITIONS; VIBRATION SIGNAL; VIBRATIONS;

ELECTRIC MACHINERY; FAULT DETECTION; FEATURE EXTRACTION; NEURAL NETWORKS; TIME DOMAIN ANALYSIS;

INDUSTRIAL ELECTRONICS;

EID: 84872904520     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IECON.2012.6389596     Document Type: Conference Paper

References (23)

1. G. Del Vescovo, M. Paschero, A. Rizzi, R. Di Salvo and F. M. F. Mascioli, "Multi-fault diagnosis of rolling-element bearings in electric machines," in Proc. IEEE ICEM, pp. 1-6, 2010.
2. H. Abu-Rub, S. M. Ahmed, A. Iqbal, M. Rahimian and H. A. Toliyat, "Incipient bearing fault diagnostics for inverter fed induction motor drive using ANFIS," in Proc. IEEE ICEM, pp.1-5, 2010.
3. J. R. Stack, T. G. Habetler and R. G. Harley, "Fault classification and fault signature production for rolling element bearings in electric machines," IEEE Trans. Ind. Appl., vol. 40, no. 3, pp. 735- 739, 2004.
4. L. Frosini, E. Bassi, A. Fazzi and C. Gazzaniga, "Use of the stator current for condition monitoring of bearings in induction motors," in Proc. IEEE ICEM, pp.1-6, 2008.
5. E. C. C. Lau and H. W. Ngan, "Detection of Motor Bearing Outer Raceway Defect by Wavelet Packet Transformed Motor Current Signature Analysis," IEEE Trans. Instrum. and Measur., vol. 59, no. 10, pp. 2683-2690, Oct. 2010.
6. F. Immovilli, A. Bellini, R. Rubini and C. Tassoni, "Diagnosis of Bearing Faults in Induction Machines by Vibration or Current Signals: A Critical Comparison," IEEE Trans. Ind. Appl., vol. 46, no. 4, pp. 1350-1359, Aug. 2010.
7. Y. Ruqiang and R. X. Gao, "Energy-Based Feature Extraction for Defect Diagnosis in Rotary Machines," IEEE Trans. Jnstrum. on Measur., vol. 58, no. 9, pp. 3130-3139, Sept. 2009.
8. B. Zhang, C. Sconyers, C. Byington, R. Patrick, M. E. Orchard and G. Vachtsevanos, "A Probabilistic Fault Detection Approach: Application to Bearing Fault Detection," IEEE Trans. Ind. Electron., vol. 58, no. 5, pp. 2011-2018, May 2011.
9. L. B. Jack, A. K. Nandi and A. C. McCormick, "Diagnosis of rolling element bearing faults using radial basis function networks," Appl. Signal Process., vol. 6, pp. 25-32, 1999.
10. R. B. W. Heng and M. J. M. Nor, "Statistical analysis of sound and vibration signals for monitoring rolling-element bearing condition," Appl. Acoust., vol. 53, pp. 211-226, 1998.
11. J. Liu, W. Wang and F. Golnaraghi, "An Enhanced Diagnostic Scheme for Bearing Condition Monitoring," IEEE Trans. Instrum. and Measur., vol. 59, no. 2, pp. 309-321, Feb. 2010.
12. A. Malhi and R.X Gao, "PCA-based feature selection scheme for machine defect classification", IEEE Trans. Instrum. and Measur., vol. 53, no. 6, pp. 1517- 1525, 2004.
13. J. Yu, "Local and Nonlocal Preserving Projection for Bearing Defect Classification and Performance Assessment," IEEE Trans Ind. Electron., vol. 59, no. 5, pp. 2363-2376, May 2012.
14. Y. Zhang, G. Xu, L. Liang and J. Wang, "Feature Extraction Methods for Fault Classification of Rolling Element Bearing Based on Nonlinear Dimensionality Reduction and SVMs," in Proc. IEEE AICI, pp. 228-234, Nov. 2009.
15. Q. Jiang, N. Jia, J. Hu and F. Xu, "Machinery fault diagnosis using supervised manifold learning," Mech. Syst. Signal Process., vol. 23, no. 7, pp. 2301-2311, Oct. 2009.
16. D. C. Baillie and J. Mathew, "A comparison of autoregressive modeling techniques for fault diagnosis of rolling element bearings," Mech. Syst. Signal Process., vol. 10, pp. 1-17, 1996.
17. I. Alguindigue, A. Loskiewicz-Buczak and R. Uhrig, "Monitoring and diagnosis of rolling element bearings using artificial neural networks," IEEE Trans. Ind. Electron., vol. 40, no. 2, pp. 209-216, 1993.
18. V. N. Ghate and S. V. Dudul, "Cascade Neural-Network-Based Fault Classifier for Three-Phase Induction Motor," IEEE Trans. on Ind. Electron., vol.58, no.5, pp.1555-1563, May 2011.
19. L. A. O Martins, F. L. C. Pádua and P. E. M. Almeida, "Automatic detection of surface defects on rolled steel using Computer Vision and Artificial Neural Networks," in Proc. IEEE IECON, pp. 1081-1086, Nov. 2010.
20. P. Demartines and J. Herault, "Curvilinear Component Analysis: a Self-Organising Neural Network for Non-Linear Mapping of Data Sets", IEEE Trans. Neural Networks, vol. 8, no. 1, pp. 148-154, 1997.
21. G.Cirrincione and M. Cirrincione, Neural Based Orthogonal Data Fitting: The EXIN Neural Networks, Wiley and Sons (New York, USA), series : Adaptive and Learning Systems for Signal Processing, Communications and Control, November 2010.
22. C. Bishop, Pattern Recognition and Machine Learning, Ed. Springer, 2007.
23. A. Guérin-Dugué, P. Teissier, G. Delso-Gafaro. and J. Hérault, "Curvilinear Component Analysis for High-dimensional Data Representation: II. Examples of introducing additional mapping constraints for specific applications", in Proc. IWANN, Lecture Notes in Computer Science 1607, pp. 635-644, Springer-Verlag, 1999.