Cross-domain Emerging Fault Diagnosis of Rotating Machinery Using Source-free Self-supervised Domain Adaptation Network

doi:10.3901/JME.2025.03.067

Abstract

Abstract: With the rapid advancement of next-generation artificial intelligence technologies, data-driven intelligent fault diagnosis methods have found widespread applications in mechanical equipment. High-precision diagnosis of intelligent diagnostic models typically relies on a large volume of labeled data. However, unpredictable new faults may occur during the operation of mechanical equipment, which makes it difficult to adopt the model trained on known samples to accurately identify newly occurring faults. Furthermore, data privacy restricts the accessibility of data, adding substantial complexity to the domain adaptation process for diagnostic models. To address these challenges, a source-free self-supervised domain adaptation network is proposed for cross-domain emerging fault diagnosis of rotating machinery, which enables diagnosis emerging fault in target domain without access to source domain. First, a source domain fault diagnosis model is established using labeled source samples. Subsequently, a self-supervised pseudo-labeling technique based on uncertainty information entropy is utilized to acquire a target domain dataset with high-quality pseudo-labels. Finally, we merge the pseudo-labeled fault dataset with unlabeled fault dataset, then train the model through an adversarial training strategy to realize emerging fault detection. The effectiveness of the proposed method is validated through experiments conducted on an automotive gearbox dataset. Experimental results show that the proposed method can accurately detect emerging faults while maintaining the performance in classifying known fault types.

Key words: intelligent fault diagnosis, source-free domain adaptation, emerging fault, self-supervised learning, transfer learning

CLC Number:

TG156

YUE Ke, LI Jipu, CHEN Zhuyun, HE Guolin, DENG Shuhan, LI Weihua. Cross-domain Emerging Fault Diagnosis of Rotating Machinery Using Source-free Self-supervised Domain Adaptation Network[J]. Journal of Mechanical Engineering, 2025, 61(3): 67-76.

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URL: http://www.cjmenet.com.cn/EN/10.3901/JME.2025.03.067

http://www.cjmenet.com.cn/EN/Y2025/V61/I3/67

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