Rolling Bearing Fault Diagnosis Based on Refined Composite Multi-Scale Approximate Entropy and Optimized Probabilistic Neural Network

Jianpeng Ma; Zhenghui Li; Chengwei Li; Liwei Zhan; Guang-Zhu Zhang

doi:10.3390/e23020259

Rolling Bearing Fault Diagnosis Based on Refined Composite Multi-Scale Approximate Entropy and Optimized Probabilistic Neural Network

Entropy (Basel). 2021 Feb 23;23(2):259. doi: 10.3390/e23020259.

Authors

Jianpeng Ma¹, Zhenghui Li², Chengwei Li¹, Liwei Zhan², Guang-Zhu Zhang³

Affiliations

¹ School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150001, China.
² Aero Engine Corporation of China Harbin Bearing Co., LTD, Harbin 150500, China.
³ Songsin Global Campus, Undergraduate College, The Catholic University of Korea, Bucheon-Si, Gyeonggi-do 14662, Korea.

Abstract

A rolling bearing early fault diagnosis method is proposed in this paper, which is derived from a refined composite multi-scale approximate entropy (RCMAE) and improved coyote optimization algorithm based probabilistic neural network (ICOA-PNN) algorithm. Rolling bearing early fault diagnosis is a time-sensitive task, which is significant to ensure the reliability and safety of mechanical fault system. At the same time, the early fault features are masked by strong background noise, which also brings difficulties to fault diagnosis. So, we firstly utilize the composite ensemble intrinsic time-scale decomposition with adaptive noise method (CEITDAN) to decompose the signal at different scales, and then the refined composite multi-scale approximate entropy of the first signal component is calculated to analyze the complexity of describing the vibration signal. Afterwards, in order to obtain higher recognition accuracy, the improved coyote optimization algorithm based probabilistic neural network classifiers is employed for pattern recognition. Finally, the feasibility and effectiveness of this method are verified by rolling bearing early fault diagnosis experiment.

Keywords: coyote optimized algorithm; fault diagnosis; probabilistic neural network; refined composite multi-scale approximate entropy; rolling bearing.