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Fault diagnosis of planetary gearboxes based on HRCMFDE、 LS and BA-SVM

Published:2023-01-30 author:ZHUANG Min, LI Ge, FAN Zhi-jun, et al. Browse: 1088 Check PDF documents

Fault diagnosis of planetary gearboxes based on

HRCMFDE、 LS and BA-SVM

ZHUANG Min1, LI Ge2, FAN Zhi-jun3, KONG De-cheng4

(1.Intelligent Manufacturing College, Hangzhou Polytechnic, Hangzhou 311402, China;

2.School of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China;

3.College of Mechanical and Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China;

4.Zhengzhou Machinery Research Institute Co., Ltd., Zhengzhou 450052, China)

Abstract: Aiming at the question of characteristics extraction and fault diagnosis of planetary gearbox, a fault diagnosis solution based on hybrid refined composite multiscale fluctuation dispersion entropy (HRCMFDE), Laplacian score (LS) and bat algorithm optimized support vector machine (BA-SVM) was raised. Firstly, HRCMFDE, a new time series complexity measurement method, was presented. It was composed of five RCMFDEs with different coarse grained methods, which had more comprehensive and reliable feature extraction performance,and was used to mine the fault information reflecting the state of planetary gearbox from the vibration signal to form the initial mixed fault feature. Then, considering that the fault features composed of HRCMFDE had high dimension and redundancy, Laplacian score was used to optimize the initial features to generate low-dimensional sensitive features. Finally, bat algorithm optimized support vector machine (BA-SVM) was used to train and classify different fault feature vectors of the planetary gear train, and the methods based on HRCMFDE, LS, and BASVM were verified by using the real fault data set.The results show that the effectiveness experiment of the proposed scheme using the planetary gearbox data set can accurately identify the different faults of the gearbox,the accuracy of single classification is 98.13%, the average accuracy of multiple classification is also better than the comparison method. The results verify the effectiveness of the hybrid refined composite multiscale fluctuation dispersion entropy feature extraction, and can provide a supplementary method for fault diagnosis of planetary gearbox.

Key words: feature extraction; feature dimensionality reduction optimization; fault classification and identification; hybrid refined composite multi-scale fluctuation dispersion entropy(HRCMFDE); Laplacian score (LS); bat algorithm optimized support vector machine (BA-SVM)

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