基于卷积双向长短期记忆网络与混沌理论的滚动轴承故障诊断

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Abstract The traditional rolling bearing fault diagnosis methods are difficult to diagnose in the environment of large noise and variable load. Based on chaos theory, CCNN-BiLSTM intelligent fault diagnosis method is proposed by using convolutional neural network (CNN) and bidirectional long short memory network (BiLSTM). The one-dimensional time series was transformed into two-dimensional chaotic series by the phase space reconstruction method, and the effective nonlinear information in the chaotic sequence was learned and extracted, which was input into the Softmax layer to complete the classification. The result show that, compared with the existing methods, the accuracy of the proposed CCNN-BiLSTM method is 3.76% and 5.21% higher under variable load and high noise (SNR=-8dB) environment respectively, which indicates that the proposed method has good robustness and generalization performance.
Key words: convolutional neural network; bi-directional long short term memory networks; chaos; bearing; fault diagnosis

Key words： convolutional neural network bi-directional long short term memory networks chaos bearing fault diagnosis

Received: 12 May 2021 Published: 15 September 2022

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	Articles by authors
	JIN Jiangtao1
	XU Zifei1
	LI Chun1
	2
	MIAO Weipao1
	SUN Kang1
	XIAO Junqing1

Cite this article:

JIN Jiangtao1,XU Zifei1,LI Chun1, et al. Fault diagnosis of rolling bearing based on CCNN-BiLSTMN method[J]. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(17): 160-169.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2022/V41/I17/160

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