基于同步挤压S变换和集成深层脊波自编码器的轴承故障诊断

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Abstract Aiming at the problems of traditional fault diagnosis algorithms of rolling bearings having such shortcomings as largely dependent on expert prior knowledge and difficulty in fault feature extraction, a method based on synchrosqueezed S transform (SSST) and ensemble deep ridgelet auto-encoder (EDRAE) was proposed. Firstly, the vibration signals were transformed by SSST to get time-frequency images. Then time-frequency images were compressed by two-directional two dimensional principal components analysis (TD-2DPCA). Secondly, different ridgelet auto-encoders (RAEs) with different ridgelet functions were designed. Then, different deep ridgelet auto-encoders (DRAEs) with different RAEs were designed and a "cross-layer" connection was introduced to alleviate gradient disappearance of DRAEs. Finally, the compressed images were input into each DRAE for unsupervised pre-training and supervised fine-tuning, and the recognition result was given by the weighted averaging method. Experimental results show that the proposed method can effectively identify the bearing faults under multiple working conditions and multiple fault severities. The proposed method has better ability of feature extraction and recognition than artificial neural network, deep belief network, deep auto-encoder and so on.

Key words： synchrosqueezed S transform ridgelet auto-encoder rolling bearing fault diagnosis

Received: 31 January 2019 Published: 15 July 2020

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	Articles by authors
	DU Xiaolei1
	2
	CHEN Zhigang1
	2
	WANG Yanxue1
	2

Cite this article:

DU Xiaolei1,2,CHEN Zhigang1, et al. Bearing fault diagnosis based on the synchrosqueezed S transform and ensemble deep ridgelet auto-encoder[J]. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(14): 59-68.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2020/V39/I14/59

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