改进CDCGAN的发电机轴承故障诊断方法

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Abstract In actual working conditions, the fault sample data collected by sensors in the generator is limited, and there is over-fitting problem in fault diagnosis based on deep learning method, which leads to poor generalization ability of the model and low diagnosis accuracy. In order to solve this problem, this paper adopts the idea of sample expansion, and proposes an improved fault diagnosis method of auxiliary classifier Conditional Deep Convolutive Generative Adversarial Network (MACDCGAN). By enhancing the characteristics of the collected one-dimensional time series signals by wavelet transform, the conditional depth convolution of simplified structural parameters is constructed to generate the samples of the countermeasure network model, and the Wasserstein distance optimization loss function is used in the model to solve the shortcomings of pattern collapse and gradient disappearance in the training process. An independent classifier is added to improve the compatibility of the classification model, and the learning rate attenuation algorithm is introduced into the classifier to increase the stability of the model. The experimental results show that this method can effectively improve the accuracy of fault diagnosis, and verify that the proposed model has good generalization performance.

Key words： Generator Feature extraction Generative Adversarial Networks Convolutional Neural Network Fault diagnosis

Received: 25 May 2023 Published: 15 June 2024

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	Articles by authors
	CAO Jie1
	2
	YIN Haonan1
	WANG Jinhua1
	2

Cite this article:

CAO Jie1,2,YIN Haonan1, et al. Fault diagnosis of generator bearings based on MACDCGAN[J]. JOURNAL OF VIBRATION AND SHOCK, 2024, 43(11): 227-235.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2024/V43/I11/227

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