Supervised Contrastive Learning with Multi-scale Attention Mechanism for Fault Diagnosis of Bearing under Variable Operating Conditions

Abstract

Bearing is a key component of rotating machinery, thus the accurate and efficient fault diagnosis of bearing is critical to the safety and reliability of rotating machinery systems. In recent years, the intelligent fault diagnosis technology of bearing represented by deep learning has made rapid development. However, the stability and generalization performance of the fault diagnosis model of bearing under complex environmental noise and variable working conditions is not good. Fortunately, self-supervised learning represented by contrastive learning can provide a good solution. The current research on contrastive learning mainly focuses on self-supervised contrastive learning, but the lack of supervised learning of fault label information leads to the reduction of diagnostic accuracy and robustness under variable working conditions. To solve this problem, this paper proposes a supervised contrastive learning combined with multi-scale attention mechanism for fault diagnosis of bearing under variable operating conditions. Firstly, the preprocessing of bearing time-domain vibration data is completed, including data set division, data augmentation, and construction of “positive” or “negative” pairs for comparative learning. Then, a supervised learning network integrating multi-scale attention mechanism is constructed to complete bearing fault feature extraction. Finally, the model transfer fault diagnosis is completed under complex noise and variable working conditions. The performance of the proposed method is evaluated through a large number of noise and variable-condition experiments. The results show that the proposed method has high fault classification accuracy and robustness under the influence of complex noise and variable working conditions.