《机电工程》杂志社科技期刊核心期刊论文投稿机电一体化机械、仪表电工自动化、计算机浙江大学主办

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Fault diagnosis strategy of rotating machinery based on CMIE and parameter optimization KELM

Published:2024-01-29 author:LIAN Pu, WU Lei, WU Yonghao. Browse: 212 Check PDF documents

Fault diagnosis strategy of rotating machinery based on CMIE

and parameter optimization KELM

LIAN Pu1, WU Lei2, WU Yonghao3

(1.Department of Mechanical and Electrical Engineering, Changzhi Vocational and Technical College, Changzhi 046000, China;

2.School of Mechanical Engineering, Xi ’an Jiaotong University, Xi 'an 710049, China; 3.Joint Transmission and Bearing

Technology Research Center, Shizuishan 753000, China)

Abstract: Aiming at the problem that multiscale permutation entropy ignored signal amplitude information and coarse-grained processing was insufficient, which led to unstable and unreliable fault identification accuracy of rotating machinery, a new fault diagnosis strategy based on composite multiscale increment entropy (CMIE) and arithmetic optimization algorithm (AOA) optimized kernel extreme learning machine (KELM) for rotating machinery was proposed. Firstly, introducing increment entropy instead of permutation entropy for fault feature extraction and using composite coursed processing for multi-scale analysis of signals, a composite multiscale increment entropy index was proposed for extracting nonlinear fault features of rotating machinery vibration signals. Then, the core parameters of KELM were adaptive optimized by AOA, and the optimal classification model of network structure was established. Finally, the fault features were input to the AOA-KELM classifier for training and testing, and the fault identification of samples was realized according to the output label of the classifier. The performance of the proposed strategy was tested and analyzed using rotating machinery fault data sets. The research results indicate that the CMIE method can effectively identify the types and degrees of faults in rotating machinery, and the recognition accuracy of both datasets reaches 99.2%, which is superior to the comparative method in terms of feature extraction efficiency and recognition accuracy. The recognition accuracy and efficiency of AOAKELM are better than that of genetic algorithm optimized kernel extreme learning machine, particle swarm optimization optimized kernel extreme learning machine, grid search optimized kernel extreme learning machine and grey wolf algorithm optimized kernel extreme learning machine.

Key words: composite multiscale increment entropy(CMIE); arithmetic optimization algorithm(AOA); kernel extreme learning machine(KELM); rotating bearing; gearbox; composite coursed processing; signals multi-scale analysis

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