Rolling Bearing Degradation State Identification Based on LCD Relative Spectral Entropy
- 140 Downloads
In the interest of obtaining an effective bearing degradation feature from complex, nonlinear, and nonstationary vibration signals, a new analytical methodology based on local characteristic-scale decomposition (LCD) and relative entropy theory is proposed. On the one hand, LCD is a new and relatively excellent time-frequency analysis method to analyze practical vibration signals polluted by noise. On the other hand, relative entropy theory is a good way to characterize different degradation states by calculating the probability distribution difference between the degradation signals and the normal signal. Combining the above two theories, two new degradation features named LRNE and LRQE are extracted to indicate the bearing degradation trend from normal state to even failure state. The noise resistance ability and extensive applicability of both the features are verified by simulation signal. For further analysis of experimental vibration signals, the two features have a satisfying performance to characterize different bearing degradation states. With the help of gray relational analysis and fuzzy C-means clustering, the proposed two characteristics can identify different bearing degradation states of inner ring fault mode with high accuracy. In the end, the two features are applied to doing bearing failure analysis with the full-life bearing data. The results show that the LRNE and LRQE are sensitive to bearing degradation trend in the whole life of bearing.
KeywordsRolling bearing Degradation state identification LCD relative spectral entropy Fuzzy C-means clustering Gray relational analysis Failure analysis
This project is supported by National Natural Science Foundation of China (Grant No. 51541506).
- 10.Y.X. Wang, Z.J. He, Y.Y. Zi, A comparative study on the local mean decomposition and empirical mode decomposition and their applications to rotating machinery health diagnosis. J. Vib. Acoust. 132(2), 613–624 (2010)Google Scholar
- 11.Y.X. Wang, Z.J. He, Y.Y. Zi, A demodulation method based on improved local mean decomposition and its application in rub- impact fault diagnosis. Meas. Sci. Technol. 20(2), 1–10 (2009)Google Scholar
- 12.J. Cheng, J. Zheng, Y. Yang, A nonstationary signal analysis approach—the local characteristic-scale decomposition method. J. Vibr. Eng. (P.R.China) 25(2), 215–220 (2012)Google Scholar
- 13.Y. Yang, M. Zeng, J. Cheng, Research on local characteristic-scale decomposition and its capacities. J. Vibr. Eng. (P.R.China) 25(5), 602–608 (2012)Google Scholar
- 16.J. Cheng, J.-D. Zheng, Yanshan Yang. Fault diagnosis model for rolling bearing based on partly ensemble local characteristic-scale decomposition and Laplacian score. J. Vibr. Eng. 27(6), 942–950 (2014)Google Scholar
- 17.Yukui Wang, Hongru Li, Bing Wang et al., Spatial Information Entropy and Its Application in the Degradation State Identification of Hydraulic Pump. Math. Probl. Eng. 2015(7), 1–11 (2015)Google Scholar
- 19.H.L. Schmitt, L.R.B. Silva, P.R. Scalassara, et al. Bearing fault detection using relative entropy of wavelet components and artificial neural networks. in IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics & Drives. 2013, pp. 538–543Google Scholar
- 20.Y. Yang, M. Zeng, J. Cheng, A new time-frequency analysis method—the local characteristic-scale decomposition. Hunan Univ. (P.R.China) 39(6), 35–39 (2012)Google Scholar
- 21.D. Xu, Y. Xu, X. Chen et al., Residual fatigue life prediction based on grey model and EMD. J. Vibr. Eng. (P.R.China) 24(1), 104–110 (2011)Google Scholar
- 22.Loparo K. A. Case Western Reserve University Bearing Data Center [EB/OL]. http://www.eecs.cwru.edu/laboratory/bearing.
- 23.X.I.N.G. HongJie, H.U.A. Baogang, An adaptive fuzzy C-mean clustering-based mixture of experts model for unlabeled data classification. Nero Comput. 71, 1008–1021 (2008)Google Scholar