Use of generalized refined composite multiscale fractional dispersion entropy to diagnose the faults of rolling bearing
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ORIGINAL PAPER
Use of generalized refined composite multiscale fractional dispersion entropy to diagnose the faults of rolling bearing Jinde Zheng
. Haiyang Pan
Received: 22 August 2019 / Accepted: 13 July 2020 Springer Nature B.V. 2020
Abstract A typical symptom of vibration signals collected from rolling bearings with local faults is the existence of periodic transients, which makes the intrinsic structure of vibration signals become more and more regular. Generally, the vibration always contains multiple intrinsic oscillatory modes on different scales, which generally is caused by the interaction and coupling of machine components. Therefore, it is necessary to detect the behavior change of complexity of vibration signals in the view of multiple scales for fault information representation. The complexity and nonlinear failure symptom of rolling bearing can be evaluated by the recently proposed nonlinear dynamic tools, such as multiscale entropy (MSE) and its variants. Recently, the improved MSE method, multiscale dispersion entropy (MDE) and its improvement refined composite MDE (RCMDE) are developed to measure the complexity of time domain data. However, the intrinsic shortages of coarse graining approach that used in MDE and RCMDE have limited their application to fault feature representation. In this paper, an improved RCMDE J. Zheng (&) H. Pan School of Mechanical Engineering, Anhui University of Technology, Maanshan 243032, Anhui, China e-mail: [email protected] J. Zheng School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW 2052, Australia
approach named generalized refined composite multiscale fluctuation-based fractional dispersion entropy (GRCMFDE) is proposed to enhance MDE and RCMDE in complexity measurement of time series. GRCMFDE was compared with MPE, MDE, RCMDE by analyzing synthetic simulation signals to verify its advantages. After that, an intelligent fault diagnosis method was proposed by combining GRCMFDE with supervised multi-clustering feature selection and gray wolf optimized SVM for fault classification of rolling bearing. Lastly, the proposed fault diagnostic method was applied to two experimental data set analysis by comparing with multiscale permutation entropy, MDE- and RCMDE-based fault diagnostic methods and the comparison results indicate that the proposed method can effectively diagnose the fault locations and severities of rolling bearing and get a higher fault identifying rate than the comparative methods. Keywords Complexity Fault diagnostic method Multiscale dispersion entropy Multiscale permutation entropy Rolling bearing
1 Introduction Rolling bearing has been a key part of many rotating machines and other equipment; meanwhile, it is also the device most prone to failure. When the rolling
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J. Zheng, H. Pan
bearing emerges local faults, a typical symptom of their vibration signals is the existence of periodic transients [1], which makes the intrinsic structure of vi
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