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Fault diagnosis method of rotating machinery based on HMFDE and t-SNE

Published:2024-06-27 author:YIN Jiu, ZHANG Jie. Browse: 49 Check PDF documents

Fault diagnosis method of rotating machinery based on HMFDE and tSNE

YIN Jiu1,ZHANG Jie2

(1.School of Mechanical and Electrical Engineering, Hubei Light Industry Technology Institute, Wuhan 430070, China;

2.School of

Mechanical and Electrical Engineering, Wuhan University of Technology, Wuhan 430070, China)

Abstract: Aiming at the problem of fault feature extraction of rotating machinery and the fault features of a single type cannot fully reflect the fault characteristics, a rotating machinery fault diagnosis method based on hybrid multi-scale fluctuation dispersion entropy (HMFDE), t-distributed stochastic neighbor embedding (t-SNE), and coyote optimization algorithm (COA)-extreme learning machine (ELM) was proposed. Firstly, a hybrid multiscale fluctuation dispersion entropy method was proposed by using feature weighting and used to extract fault characteristics of vibration signals of rotating machinery. Then, the feature dimension reduction of mixed fault features was carried out by using t-SNE method, and the feature subset that could best reflect fault characteristics was selected to construct sensitive feature samples. Finally, coyote optimization algorithm was used to optimize the input weight value and hidden layer threshold of the extreme learning machine to realize fault identification and classification of rotating machinery. Taking gearbox and rolling bearing fault data set as objects, experiments were conducted on the fault diagnosis methods based on HMFDE, t-SNE and COA-ELM, and the effectiveness of the mothods was overified. The results show that the HMFDE-t-SNE-CAO-ELM fault diagnosis method achieves 100% fault identification accuracy and can effectively diagnose different fault types and damage of rotating machinery. Comparing to fault diagnosis methods based on single type features, the accuracy has been improved by 0.68 %, 22.42 %, 29.18% (gearbox) and 1.43%, 8.23%, 23.67%(rolling bearing), respectively. Although some efficiency has been sacrificed, the accuracy of this method has been significantly improved. Comparing with other conventional fault classifiers, COA-ELM has obvious advantages in fault recognition accuracy.

Key words: rotating machinery; fault diagnosis; gear box; rolling bearing; hybrid multi-scale fluctuation dispersion entropy(HMFDE); t-distributed stochastic neighbor embedding(t-SNE); coyote optimization algorithm(COA); extreme learning machine(ELM)

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