Citation

Gu Y, Liu P, Lin Z, Qiu G. China Saf. Sci. J. 2021; 31(6): 99-105.

Copyright

(Copyright © 2021, China Occupational Safety and Health Association, Publisher Gai Xue bao)

DOI

10.16265/j.cnki.issn1003-3033.2021.06.013

PMID

unavailable

Abstract

In order to reveal fault characteristics of different bearing and improve accuracy and efficiency of fault diagnosis, a bearing fault diagnosis method based on Kurtogram and DSCN was proposed. After Kurtogram was generated with original vibration signals, its graphic features under different fault modes were studied and recognized by DSCN, and advantageous features were automatically extracted for fault classification. The results show that compared with other fault diagnosis methods, the proposed one has the highest recognition accuracy on test set, reaching as far as 97. 28%, which also reflects obvious advantages of DSCN in reducing number of parameters and increasing training speed. © 2021 China Safety Science Journal. All rights reserved.

Language: zh