Fault diagnosis method based on slip information entropy and optimal filter construction

PDF(920 KB)

Journal of Vibration and Shock ›› 2017, Vol. 36 ›› Issue (21) : 34-39.

Shuiguang Tong1, Jian Xu2 , Feiyun Cong1, Ning Tang2, Yidong Zhang1

Author information +

History +

Abstract

Based on the difference in local feature of fault signal，the concept of slip information entropy sequence is put forward combined with the phase space reconstruction and information entropy theory to detect the local impulse feature information. The minimum entropy deconvolution, optimal filter construction theory are applied to improve the ability of weak fault diagnosis of rolling bearings. The proposed method is successfully applied into the fault feature extraction of rolling bearings. The experimental data analysis result shows that the proposed method has a good ability at weak shock fault feature extraction. The work of this paper has important implications in fault intelligent diagnosis of rolling bearings under strong noise background.

Key words

Information entropy / slip interception / optimal filter / feature extraction / rolling bearing

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Shuiguang Tong1, Jian Xu2,Feiyun Cong1, Ning Tang2, Yidong Zhang1. Fault diagnosis method based on slip information entropy and optimal filter construction[J]. Journal of Vibration and Shock, 2017, 36(21): 34-39

References

[1] Randall, Robert B, Antoni, Jerome, Rolling element bearing diagnostics—a tutorial. Mechanical Systems and Signal Processing,2011. 25(2): p. 485-520.
[2] Cong, Feiyun, Chen, Jin, Dong, Guangming, Zhao, Fagang, Short-time matrix series based singular value decomposition for rolling bearing fault diagnosis. Mechanical Systems and Signal Processing,2013. 34(1): p. 218-230.
[3] Xu, J., Tong, S., Cong, F., Zhang, Y., The application of time-frequency reconstruction and correlation matching for rolling bearing fault diagnosis. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science,2015. 229(17): p. 3291-3295.
[4] Pincus, Steven M, Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences,1991. 88(6): p. 2297-2301.
[5] Richman, Joshua S, Moorman, J Randall, Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology-Heart and Circulatory Physiology, 2000. 278(6): p. H2039-H2049.
[6] 杨文献, 姜节胜, 机械信号奇异熵研究. 机械工程学报,2000(12): p. 9-13.
Wenxian, Yang, Jiesheng, Jiang, Study on the singular entropy of mechanical signal. Chinese Journal of Mechanical Engineering,2000. 36(12): p. 9-13.
[7] 张雨, 胡茑庆, 基于符号树信息熵的机械振动瞬态信号特征提取. 国防科技大学学报,2003(04): p. 79-81.
Zhang, Y., Hu, N. Q., Extraction of the characteristic of mechanism vibration transient signal based on entropy of symbolic tree. Guofang Keji Daxue Xuebao/Journal of National University of Defense Technology,2003. 25(4): p. 79.
[8] Yu, Yang, Junsheng, Cheng, A roller bearing fault diagnosis method based on EMD energy entropy and ANN. Journal of sound and vibration,2006. 294(1): p. 269-277.
[9] 夏勇, 赵红, 基于关联距离熵的诊断方法研究. 振动与冲击,2003(02): p. 78-79+105.
Xia,Y.,Zhao,H.,Leakage fault diagnosis for valve train based on correlation distance entropy.Journal of vibration and shock,2003. 22(2):p. 78-79+105.
[10] Yu, Dejie, Yang, Yu, Cheng, Junsheng, Application of time–frequency entropy method based on Hilbert–Huang transform to gear fault diagnosis. Measurement,2007. 40(9–10): p. 823-830.
[11] 郑近德, 陈敏均, 程军圣, 杨宇, 多尺度模糊熵及其在滚动轴承故障诊断中的应用. 振动工程学报,2014(01): p. 145-151.
Zheng, J. D., Chen, M. J., Cheng, J. S., Yang, Y., Multiscale fuzzy entropy and its application in rolling bearing fault diagnosis. Zhendong Gongcheng Xuebao/Journal of Vibration Engineering,2014. 27(1): p. 145-151.
[12] Zhao, X., Ye, B., Selection of effective singular values using differences pectrum and its applicationt of fault diagnosis of headstock. Mechanical Systems and Signal Processing,2011. 25: p. 1617-1631.
[13] Endo, H, Randall, RB, Application of a minimum entropy deconvolution filter to enhance Autoregressive model based gear tooth fault detection technique. Mechanical Systems and Signal Processing,2007. 21(2): p. 906-919.