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Fault diagnosis method of rolling bearing based on ALIF and 1DCNN

Published:2022-12-20 author:NIE Yong-jun, MENG Jin, XIAO Ying-nan. Browse: 508 Check PDF documents

Fault diagnosis method of rolling bearing based on ALIF and 1DCNN

NIE Yong-jun1, MENG Jin2, XIAO Ying-nan3

(1.Ddpartment of Mechanical Engineering, Guangzhou Maritime University, Guangzhou 510725, China;

2.Sichuan Vocational and Technical College of Communications, Chengdu 611130, China;

3.College of Engineering and Technology, Chengdu University of Technology, Leshan 614000, China)

Abstract: In the fault signal of rolling bearing, there were many redundant components, which will interfere with the accuracy of fault diagnosis based on one-dimensional convolutional neural network (1DCNN). Therefore, a rolling bearing fault diagnosis method based on adaptive local iterative filtering algorithm (ALIF) and 1DCNN was proposed, that was, the original signal was decomposed and reconstructed before classification.Firstly, the original signal was decomposed by the ALIF,comparing with other signal decomposition algorithms, the algorithm had less modal aliasing, which was due to maintaining its original physical properties. Its characterization information was extracted to the greatest extent and the accuracy of its fault diagnosis was improved.Then, the Pearson correlation coefficient method was used to select the intrinsic mode function (IMF)that was most correlated with the original signal for reconstruction, and obtain a signal with less redundant signals. Finally, the processed data was directly used as the input of 1DCNN for intelligent fault diagnosis. The research results show that the classification accuracy of the proposed method for the four states of the rolling bearing is improved by 8% comparing with the original method, and the classification accuracy of the proposed method reaches 99%. The superiority of the ALIF decomposition performance is proved by the simulation signal, and the advanced nature of the proposed method is verified by collecting the actual data on the experimental bench.

Key words: adaptive local iterative filtering(ALIF); onedimensional convolutional neural network(1DCNN); signal decomposition and reconstruction; failure classification; redundant information components; modal aliasing; fault diagnosis accuracy

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