基于改进半监督生成对抗网络的少量标签轴承智能诊断方法

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Abstract The big data of bearing obtained by online monitoring is mainly composed of a large amount of data without labels and a small amount of data with labels. Successful applications of many popular intelligent fault diagnosis methods rely on the supervised learning of large scale of labeled data. To solve this problem, an improved semi-supervised learning-based generative adversarial network (ISSL-GAN) is proposed in this paper. In This method, the classifier is given more powerful classification capability through the adversarial learning mode between the generator and classifier. Furthermore, the enhanced feature matching is proposed to extract deep features from the intermediate layers of the whole network and join the loss function to speed up the convergence of the training process. Meanwhile, the semi-supervised learning makes full use of few labeled data to improve the learning efficiency of the classifier. After training with plenty of unlabeled data and few labeled data, the obtained classifier can accurately discriminate the classes of analyzed data. The proposed network is applied to four experiments, each of which is to transfer from one bearing to another one for different bearings, working conditions, fault generation methods, and damage levels, and then is compared with other deep networks. The results indicate that the proposed ISSL-GAN has much higher accuracy on bearing fault diagnosis and has faster convergence speed during the training.
Key words: Deep learning; transfer learning; fault diagnosis; semi-supervised learning; unlabeled data; bearing

Key words： Deep learning transfer learning fault diagnosis semi-supervised learning unlabeled data bearing

Received: 08 July 2021 Published: 28 November 2022

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	XING Xiaosong
	GUO Wei

Cite this article:

XING Xiaosong,GUO Wei. Intelligent diagnosis method for bearings with few labelled samples based on an improved semi-supervised learning-based generative adversarial network[J]. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(22): 184-192.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2022/V41/I22/184

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