doi:10.16186/j.cnki.1673-9787.2020.1.10

Received:2019/04/19

Revised:2019/05/29

Published:2020/01/15

Applications of SST and deep ridgelet network in bearing fault diagnosis

DU Xiaolei1,2, CHEN Zhigang1,2, ZHANG Nan1, XU Xu1,2

1.Beijing University of Civil Engineering and Architecture， Beijing  100044 ， China；2.Beijing Engineering Research Center of Monitoring for Construction Safety， Beijing  100044 ， China

Abstract:The traditional fault diagnosis methods of rolling bearing have such shortcomings as largely dependent on expert prior knowledge， difficulty in fault feature extraction and selection. So that a method based on synchrosqueezed wavelet transforms （ SST ） and deep ridgelet network （ DRN） was proposed. Firstly，the bearing vibration signals were transformed by SST to get the time-frequency images. Secondly，the time-frequency images were compressed by two-directional two dimensional principal components analysis（ TD-2DPCA）.Finally，the compressed time-frequency images were sent to DRN for automatic feature extraction and fault identification. The experimental results showed that the proposed method could effectively identify the bearing faults under multiple working conditions and multiple fault severities. The proposed method had better ability of feature extraction and recognition than traditional methods such as shallow ANN，SVM， and deep learning models such as deep belief network，deep sparse auto-encoder.

Key words:rolling bearing;fault diagnosis;synchrosqueezed wavelet transform;deep ridgelet network

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