Generalized refined composite multiscale fuzzy entropy and multi-cluster feature selection based intelligent fault diagnosis of rolling bearing

Jinde Zheng; Haiyang Pan; Jinyu Tong; Qingyun Liu

doi:10.1016/j.isatra.2021.05.042

Generalized refined composite multiscale fuzzy entropy and multi-cluster feature selection based intelligent fault diagnosis of rolling bearing

ISA Trans. 2022 Apr:123:136-151. doi: 10.1016/j.isatra.2021.05.042. Epub 2021 Jun 1.

Authors

Jinde Zheng¹, Haiyang Pan², Jinyu Tong², Qingyun Liu²

Affiliations

¹ School of Mechanical Engineering, Anhui University of Technology, Maanshan, 243032, China; School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney NSW 2052, Australia. Electronic address: jdzheng@ahut.edu.cn.
² School of Mechanical Engineering, Anhui University of Technology, Maanshan, 243032, China.

PMID: 34103159
DOI: 10.1016/j.isatra.2021.05.042

Abstract

Extracting the failure related information from vibration signals is a very important aspect of vibration-based fault detection for rolling bearing Multiscale entropy and its improvement, multiscale fuzzy entropy (MFE), are significant complexity measure tools of time series. They have been successfully applied to extract vibration failure features for rolling bearing condition monitoring . However, MFE over different scales will fluctuate with increase of scale factor. A new nonlinear dynamic parameter termed generalized refined composite multiscale fuzzy entropy (GRCMFE) is firstly developed to enhance the performance of MSE and MFE in data complexity measurement. Then three algorithms are developed and compared with MSE and MFE, as well as two algorithms of generalized MFE to verify the availability and superiority by analyzing two kinds of noise signals. In addition, based on three algorithms of GRCMFE, a novel fault diagnosis approach for rolling bearing is proposed with linking multi-cluster feature selection for supervised learning and the gravitational search algorithm optimized support vector machine for failure pattern recognition. Last, the proposed fault diagnostic approach was utilized to analyze two kinds of bearing test data sets. Analysis results indicate that our proposed fault diagnosis approach could effectively extract nonlinear dynamic complexity information and gets the highest identifying rate and the best performance among the comparative approaches.

Keywords: Complexity; Fault diagnosis; Generalized refined composite multiscale entropy; Multiscale fuzzy entropy; Rolling bearing.