Smart Structures and Systems

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Recognition of rolling bearing fault patterns and sizes based on two-layer support vector regression machines

  • Shen, Changqing (Department of Systems Engineering & Engineering Management, City University of Hong Kong) ;
  • Wang, Dong (Department of Systems Engineering & Engineering Management, City University of Hong Kong) ;
  • Liu, Yongbin (School of Engineering Science, University of Science and Technology of China) ;
  • Kong, Fanrang (School of Engineering Science, University of Science and Technology of China) ;
  • Tse, Peter W. (Department of Systems Engineering & Engineering Management, City University of Hong Kong)
  • Received : 2013.01.15
  • Accepted : 2013.06.01
  • Published : 2014.03.25
⟨ Previous Next ⟩

Abstract

The fault diagnosis of rolling element bearings has drawn considerable research attention in recent years because these fundamental elements frequently suffer failures that could result in unexpected machine breakdowns. Artificial intelligence algorithms such as artificial neural networks (ANNs) and support vector machines (SVMs) have been widely investigated to identify various faults. However, as the useful life of a bearing deteriorates, identifying early bearing faults and evaluating their sizes of development are necessary for timely maintenance actions to prevent accidents. This study proposes a new two-layer structure consisting of support vector regression machines (SVRMs) to recognize bearing fault patterns and track the fault sizes. The statistical parameters used to track the fault evolutions are first extracted to condense original vibration signals into a few compact features. The extracted features are then used to train the proposed two-layer SVRMs structure. Once these parameters of the proposed two-layer SVRMs structure are determined, the features extracted from other vibration signals can be used to predict the unknown bearing health conditions. The effectiveness of the proposed method is validated by experimental datasets collected from a test rig. The results demonstrate that the proposed method is highly accurate in differentiating between fault patterns and determining their fault severities. Further, comparisons are performed to show that the proposed method is better than some existing methods.

Keywords

References

  1. Caesarendra, W., Widodo, A. and Yang, B.S. (2011), "Combination of probability approach and support vector machine towards machine health prognostics", Probabilist. Eng. Mech., 26(2), 165-73. https://doi.org/10.1016/j.probengmech.2010.09.008
  2. Cai, G.G., Chen, X.F., Li, B., Chen, B.J. and He, Z.J. (2012), "Operation reliability assessment for cutting tools by applying a proportional covariate model to condition monitoring information", Sensors, 12(10), 12964-12987. https://doi.org/10.3390/s121012964
  3. Di Maio, F., Hu, J., Tse, P., Pecht, M., Tsui, K. and Zio, E. (2011), "Ensemble-approaches for clustering health status of oil sand pumps", Expert Syst. Appl., 39(5), 4847-4859.
  4. Gryllias,K.C., and Antoniadis, I.A. (2012), "A support vector machine approach based on physical model training for rolling element bearing fault detection in industrial environments", Eng. Apl. Artif. Intel., 25(2), 326-344. https://doi.org/10.1016/j.engappai.2011.09.010
  5. Heng, A., Zhang, S., Tan, C.C. and Mathew, J. (2009), "Rotating machinery prognostics: State of the art, challenges and opportunities", Mech. Syst. Signal Pr., 23(3), 724-739. https://doi.org/10.1016/j.ymssp.2008.06.009
  6. Hu, Q., He, Z.J., Zhang, Z.S. and Zi, Y.Y. (2007), "Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMS ensemble", Mech. Syst. Signal Pr., 21(2), 688-705. https://doi.org/10.1016/j.ymssp.2006.01.007
  7. Huang, N.E., Shen, Z. and Long, S.R. (1998), "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis", Proceedings of the Royal Society of London.
  8. Jiang, H. and He, W.W. (2012), "Grey relational grade in local support vector regression for financial time series prediction", Expert Syst. Appl., 39(3), 2256-2262. https://doi.org/10.1016/j.eswa.2011.07.100
  9. Jiang, Q., Li, T., Yao, Y. and Cai, J.H. (2012), "Study of rolling bearing SVM pattern recognition based on correlation dimension of IMF", Proceedings of the 2nd International Conference on Intelligent System Design and Engineering Application (ISDEA).
  10. Konar, P. and Chattopadhyay, P. (2011), "Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs)", Appl. Soft. Comput., 11(6), 4203-4211. https://doi.org/10.1016/j.asoc.2011.03.014
  11. Lei, Y.G., He, Z.J., Zi, Y.Y. and Chen, X.F. (2008), "New clustering algorithm-based fault diagnosis using compensation distance evaluation technique", Mech. Syst. Signal Proc., 22(2), 419-435. https://doi.org/10.1016/j.ymssp.2007.07.013
  12. Loparo, K.A. (2012), Case western reserve university bearing data center: http://csegroups.case.edu/bearingdatacenter/home.
  13. Minh, H.Q., Niyogi, P. and Yao, Y. (2006), "Mercer's theorem, feature maps, and smoothing", Proceedings of the 19th Annual Conference on Learning Theory, Pittsburgh, United states.
  14. Rafiee, J., Arvani, F., Harifi, A. and Sadeghi, M.H. (2007), "Intelligent condition monitoring of a gearbox using artificial neural network", Mech. Syst. Signal Pr., 21(4), 1746-1754. https://doi.org/10.1016/j.ymssp.2006.08.005
  15. Shen, Z.J., He, Z.J., Chen, X.F., Sun, C. and Liu, Z.W. (2012), "A monotonic degradation assessment index of rolling bearings using fuzzy support vector data description and running time", Sensors, 12(8), 10109-10135. https://doi.org/10.3390/s120810109
  16. Smola, A.J. and Schölkopf, B. (2004), "A tutorial on support vector regression", Stat. Comput., 14(3), 199-222. https://doi.org/10.1023/B:STCO.0000035301.49549.88
  17. Vapnik, V.N. (1995), The nature of statistical learning theory, Springer, Berlin.
  18. Wang, D., Miao, Q. and Kang, R. (2009), "Robust health evaluation of gearbox subject to tooth failure with wavelet decomposition", J. Sound Vib., 324(3-5), 1141-1157. https://doi.org/10.1016/j.jsv.2009.02.013
  19. Wang, D., Tse, P., Guo, W. and Miao, Q. (2011), "Support vector data description for fusion of multiple health indicators for enhancing gearbox fault diagnosis and prognosis", Meas. Sci. Technol., 22(2), 025102. https://doi.org/10.1088/0957-0233/22/2/025102
  20. Wang, S.B., Huang, W.G. and Zhu, Z.K. (2011), "Transient modeling and parameter identification based on wavelet and correlation filtering for rotating machine fault diagnosis", Mech. Syst. Signal Pr., 25(4), 1299-1320. https://doi.org/10.1016/j.ymssp.2010.10.013
  21. Widodo, A. and Yang, B.S. (2007), "Support vector machine in machine condition monitoring and fault diagnosis", Mech. Syst. Signal Pr., 21(6), 2560-2574. https://doi.org/10.1016/j.ymssp.2006.12.007
  22. Wong, W.T., and Hsu, S.H. (2006), "Application of SVM and ANN for image retrieval", Eur. J. Oper. Res., 173(3), 938-950. https://doi.org/10.1016/j.ejor.2005.08.002
  23. Yan, R.Q. and Gao, R. (2009), "Base wavelet selection for bearing vibration signal analysis", Int. J. Wavelets Multiresolut. Inf. Process., 7(4), 411-426. https://doi.org/10.1142/S0219691309002994
  24. Zhao, S.L. and Zhang, Y.C. (2008), "SVM classifier based fault diagnosis of the satellite attitude control system", Proceedings of the 2008 International Conference on Intelligent Computation Technology and Automation.
  25. Zhu, D.C., Feng, Y.P., Chen, Q. and Cai, J.B. (2010), "Image recognition technology in rotating machinery fault diagnosis based on artificial immune", Smart. Struct. Syst., 6(4), 389-403. https://doi.org/10.12989/sss.2010.6.4.389

Cited by

  1. Fault severity recognition of aviation piston pump based on feature extraction of EEMD paving and optimized support vector regression model vol.67, 2017, https://doi.org/10.1016/j.ast.2017.03.039
  2. A data mining approach for machine fault diagnosis based on associated frequency patterns vol.45, pp.3, 2016, https://doi.org/10.1007/s10489-016-0781-3
  3. Deep Fault Recognizer: An Integrated Model to Denoise and Extract Features for Fault Diagnosis in Rotating Machinery vol.7, pp.12, 2017, https://doi.org/10.3390/app7010041
  4. A fault diagnosis method of rolling element bearings based on CEEMDAN vol.231, pp.10, 2017, https://doi.org/10.1177/0954406215624126
  5. Phase Space Similarity as a Signature for Rolling Bearing Fault Diagnosis and Remaining Useful Life Estimation vol.2016, 2016, https://doi.org/10.1155/2016/5341970
  6. An Adaptive Support Vector Regression Machine for the State Prognosis of Mechanical Systems vol.2015, 2015, https://doi.org/10.1155/2015/469165
  7. Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis vol.93, 2016, https://doi.org/10.1016/j.measurement.2016.07.054
  8. Seismic response of soil-structure interaction using the support vector regression vol.63, pp.1, 2014, https://doi.org/10.12989/sem.2017.63.1.115
  9. Failure prediction of a motor-driven gearbox in a pulverizer under external noise and disturbance vol.22, pp.2, 2014, https://doi.org/10.12989/sss.2018.22.2.185
  10. Modification of acceleration signal to improve classification performance of valve defects in a linear compressor vol.23, pp.1, 2014, https://doi.org/10.12989/sss.2019.23.1.071
  11. Investigation of Gas Foil Bearings With an Adaptive and Non-Linear Structure vol.13, pp.1, 2014, https://doi.org/10.2478/ama-2019-0001
  12. A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery vol.21, pp.4, 2014, https://doi.org/10.3390/e21040409
  13. Fault Classification of Low-Speed Bearings Based on Support Vector Machine for Regression and Genetic Algorithms Using Acoustic Emission vol.7, pp.5, 2014, https://doi.org/10.1007/s42417-019-00143-y
  14. Fault Classification of Low-Speed Bearings Based on Support Vector Machine for Regression and Genetic Algorithms Using Acoustic Emission vol.7, pp.5, 2014, https://doi.org/10.1007/s42417-019-00143-y
  15. Adaptive Fisher-Based Deep Convolutional Neural Network and Its Application to Recognition of Rolling Element Bearing Fault Patterns and Sizes vol.2020, pp.None, 2020, https://doi.org/10.1155/2020/3409262
  16. Defect classification of refrigerant compressor using variance estimation of the transfer function between pressure pulsation and shell acceleration vol.25, pp.2, 2014, https://doi.org/10.12989/sss.2020.25.2.255
  17. Performance Degradation Prediction Based on a Gaussian Mixture Model and Optimized Support Vector Regression for an Aviation Piston Pump vol.20, pp.14, 2020, https://doi.org/10.3390/s20143854
  18. Parameter Optimisation in the Vibration-Based Machine Learning Model for Accurate and Reliable Faults Diagnosis in Rotating Machines vol.8, pp.4, 2020, https://doi.org/10.3390/machines8040066
  19. Employing TLBO and SCE for optimal prediction of the compressive strength of concrete vol.26, pp.6, 2014, https://doi.org/10.12989/sss.2020.26.6.753
  20. A spiking neural network-based approach to bearing fault diagnosis vol.61, pp.None, 2014, https://doi.org/10.1016/j.jmsy.2020.07.003