A self-attention based contrastive learning method for bearing fault diagnosis

• Published in: Expert systems with applications Vol. 238; p. 121645
• Main Authors: Cui, Long, Tian, Xincheng, Wei, Qingzhe, Liu, Yan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.03.2024
• Subjects: Contrastive learning; Fault diagnosis; Self-attention mechanism; Self-supervised learning; Fault diagnosis; Self-attention mechanism; Contrastive learning; Self-supervised learning
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2023.121645

The shortage of labeled data is a major obstacle to the practical application of advanced fault diagnosis technologies, and the large amount of unlabeled data may be the key to solving this problem. This paper proposes a self-attention based contrastive leaning method for bearing fault diagnosis which utilizes the unlabeled data for self-supervised learning. Using the self-attention-based signal transformer as the backbone, the proposed method is able to learn feature extraction capability from a large number of unlabeled data by contrastive learning using only positive samples. Then using a small number of labeled data for fine-tuning, the proposed method can perform accurate fault diagnosis. Experiments using both run-to-failure and artificial fault vibration signal datasets show that the proposed method can not only outperform other semi-supervised or self-supervised learning methods but also exceed the accuracy of supervised learning methods in case of insufficient labels. The visualization shows the interpretability of the model and the feature extraction ability obtained from self-supervised pre-training. •A contrastive learning method that relies on only positive pairs is introduced.•A Signal Transformer based on the self-attention mechanism is proposed.•Data augmentation methods for 1D vibration signals are investigated in detail.•The diagnostic accuracy exceeds that of commonly used supervised learning methods.•Visualization shows some interpretability of the proposed method.