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Rolling Bearing Fault Diagnosis Based on Deep Learning and Autoencoder Information Fusion

Authors :
Jianpeng Ma
Chengwei Li
Guangzhu Zhang
Source :
Symmetry, Vol 14, Iss 1, p 13 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

The multisource information fusion technique is currently one of the common methods for rolling bearing fault diagnosis. However, the current research rarely fuses information from the data of different sensors. At the same time, the dispersion itself in the VAE method has asymmetric characteristics, which can enhance the robustness of the system. Therefore, in this paper, the information fusion method of the variational autoencoder (VAE) and random forest (RF) methods are targeted for subsequent lifetime evolution analysis. This fusion method achieves, for the first time, the simultaneous monitoring of acceleration signals, weak magnetic signals and temperature signals of rolling bearings, thus improving the fault diagnosis capability and laying the foundation for subsequent life evolution analysis and the study of the fault–slip correlation. Drawing on the experimental procedure of the CWRU’s rolling bearing dataset, the proposed VAERF technique was evaluated by conducting inner ring fault diagnosis experiments on the experimental platform of the self-research project. The proposed method exhibits the best performance compared to other point-to-point algorithms, achieving a classification rate of 98.19%. The comparison results further demonstrate that the deep learning fusion of weak magnetic and vibration signals can improve the fault diagnosis of rolling bearings.

Details

Language :
English
ISSN :
20738994
Volume :
14
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Symmetry
Publication Type :
Academic Journal
Accession number :
edsdoj.97b2f62fe4a84d578212547ce14305ea
Document Type :
article
Full Text :
https://doi.org/10.3390/sym14010013