Fault Diagnosis of Rolling Element Bearing Using Multi-Scale Lempel-Ziv Complexity and Mahalanobis Distance Criterion

doi:10.1007/s12204-018-1965-2

Journal of Shanghai Jiao Tong University (Science) ›› 2018, Vol. 23 ›› Issue (5): 696-701.doi: 10.1007/s12204-018-1965-2

Fault Diagnosis of Rolling Element Bearing Using Multi-Scale Lempel-Ziv Complexity and Mahalanobis Distance Criterion

YU Kun (俞昆), TAN Jiwen (谭继文), LIN Tianran (林天然)

(School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266525, Shandong, China)

出版日期:2018-10-01 发布日期:2018-10-07
通讯作者: LIN Tianran (林天然) E-mail:trlin888@163.com

Fault Diagnosis of Rolling Element Bearing Using Multi-Scale Lempel-Ziv Complexity and Mahalanobis Distance Criterion

YU Kun (俞昆), TAN Jiwen (谭继文), LIN Tianran (林天然)

(School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266525, Shandong, China)

Online:2018-10-01 Published:2018-10-07
Contact: LIN Tianran (林天然) E-mail:trlin888@163.com

摘要/Abstract

摘要： A new fault diagnosis technique for rolling element bearing using multi-scale Lempel-Ziv complexity (LZC) and Mahalanobis distance (MD) criterion is proposed in this study. A multi-scale coarse-graining process is used to extract fault features for various bearing fault conditions to overcome the limitation of the single stage coarse-graining process in the LZC algorithm. This is followed by the application of MD criterion to calculate the accuracy rate of LZC at different scales, and the best scale corresponding to the maximum accuracy rate is identified for fault pattern recognition. A comparison analysis with Euclidean distance (ED) criterion is also presented to verify the superiority of the proposed method. The result confirms that the fault diagnosis technique using a multi-scale LZC and MD criterion is more effective in distinguishing various fault conditions of rolling element bearings.

关键词: fault diagnosis, rolling element bearing, Lempel-Ziv complexity (LZC), Mahalanobis distance (MD) criterion

Abstract: A new fault diagnosis technique for rolling element bearing using multi-scale Lempel-Ziv complexity (LZC) and Mahalanobis distance (MD) criterion is proposed in this study. A multi-scale coarse-graining process is used to extract fault features for various bearing fault conditions to overcome the limitation of the single stage coarse-graining process in the LZC algorithm. This is followed by the application of MD criterion to calculate the accuracy rate of LZC at different scales, and the best scale corresponding to the maximum accuracy rate is identified for fault pattern recognition. A comparison analysis with Euclidean distance (ED) criterion is also presented to verify the superiority of the proposed method. The result confirms that the fault diagnosis technique using a multi-scale LZC and MD criterion is more effective in distinguishing various fault conditions of rolling element bearings.

Key words: fault diagnosis, rolling element bearing, Lempel-Ziv complexity (LZC), Mahalanobis distance (MD) criterion

中图分类号:

TH 165.3

YU Kun (俞昆), TAN Jiwen (谭继文), LIN Tianran (林天然). Fault Diagnosis of Rolling Element Bearing Using Multi-Scale Lempel-Ziv Complexity and Mahalanobis Distance Criterion[J]. Journal of Shanghai Jiao Tong University (Science), 2018, 23(5): 696-701.

参考文献 12

[1]	CONG F Y, CHEN J, DONG G G, et al. Short-timematrix series based singular value decomposition forrolling bearing fault diagnosis [J]. Mechanical Systemsand Signal Processing, 2013, 34: 218-230.
[2]	KOLMOGOROV A N. Three approaches to thequantitative definition of information [J]. ProblemyPeredachi Informatsii, 1965, 1(1): 3-11.
[3]	KOLMOGOROV A N. Logical basis for informationtheory and probability theory [J]. IEEE Transactionson Information Theory, 1968, 14(5): 662-664.
[4]	KOLMOGOROV A N. Combinatorial foundations ofinformation theory and the calculus of probabilities [J].Russian Mathematical Surveys, 1983, 38(4): 27-36.
[5]	LEMPEL A, ZIV J. On the complexity of finite sequences[J]. IEEE Transactions on Information Theory,1976, 22(1): 75-81.
[6]	YANG X B, JIN X Q, DU Z M, et al. A novel modelbasedfault detection method for temperature sensorusing fractal correlation dimension [J]. Building andEnvironment, 2011, 46(46): 970-979.
[7]	HE Y Y, HUANG J, ZHANG B. Approximate entropyas a nonlinear feature parameter for fault diagnosisin rotating machinery [J]. Measurement Science andTechnology, 2012, 23(4): 45603.
[8]	HAN M H, PAN J L. A fault diagnosis method combinedwith LMD, sample entropy and energy ratio forroller bearings [J]. Measurement, 2015, 76: 7-19.
[9]	YAN R Q, GAO R X. Complexity as a measure formachine health evaluation [J]. IEEE Transactions onInstrumentation and Measurement, 2004, 53(4): 1327-1334.
[10]	HONG H B, LIANG M. Fault severity assessment forrolling element bearings using the Lempel-Ziv complexityand continuous wavelet transform [J]. Journalof Sound and Vibration, 2009, 320(1/2): 452-468.
[11]	MOEENDARBARY E, NG T Y, ZANGENEH M. Dissipativeparticle dynamics: Introduction, methodologyand complex fluid applications—A review [J]. InternationalJournal of Applied Mechanics, 2012, 1(4): 737-763.
[12]	VERVERIDIS D, KOTROPOULOS C. Gaussian mixturemodeling by exploiting the Mahalanobis distance[J]. IEEE Transactions on Signal Processing, 2008,56(7): 2797-2811.

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Fault Diagnosis of Rolling Element Bearing Using Multi-Scale Lempel-Ziv Complexity and Mahalanobis Distance Criterion

Fault Diagnosis of Rolling Element Bearing Using Multi-Scale Lempel-Ziv Complexity and Mahalanobis Distance Criterion

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