基于时频增强的滚动轴承少样本故障诊断方法

doi:10.3969/j.issn.1000-1158.2023.01.03

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Abstract Aiming at the problem of low fault diagnosis accuracy due to the scarcity of fault samples of rolling bearings and the difficulty in extracting vibration features, a fault diagnosis method of rolling bearings with few samples based on time-frequency enhancement is proposed. Firstly, one-dimensional vibration signals of rolling bearings are overlapped and sampled, and a two-dimensional time-frequency matrix is constructed by using continuous wavelet transform to map the sampled signals in time-frequency domain. Secondly, the real time-frequency samples are trained by deep convolutional generative adversarial network, and the generated time-frequency samples are added to the training set. Then, the time-series convolutional network is used to fuse the deep time-frequency domain features. Finally, Softmax classifier is constructed to output states corresponding to fault categories. Simulation experiment results show that under the condition of only 10 training samples, the method in the diagnosis of rolling bearing at case western reserve university data set average accuracy of 91.00%, compared with an enhanced time-frequency method is increased by 7.56%, and the time-frequency method is validated by the measured data can significantly improve the fault diagnosis accuracy of less sample circumstances.

Key words： metrology rolling bearing fault diagnosis time-frequency enhancement less sample

Received: 09 March 2022 Published: 13 January 2023

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TB936

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	Articles by authors
	HU Xiang-dong
	LIANG Chuan
	YANG Xi

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HU Xiang-dong,LIANG Chuan,YANG Xi. A Method of Fault Diagnosis with Few Samples for Rolling Bearing Based on Time-Frequency Enhancement[J]. Acta Metrologica Sinica, 2023, 44(1): 12-20.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2023.01.03 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2023/V44/I1/12

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