Bearing fault diagnosis method based on multi-scale domain adaptative network across operating conditions. (1st March 2023)

• Record Type: Journal Article
• Authors: Wang, Gongxian; Fu, Ze; Hu, Zhihui; Zhang, Miao; Lu, Guanghao
• Is Part Of: Measurement science & technology. Volume 34:Number 3(2023)
• Publication Date: 2023-03-01
• Subjects: bearing fault diagnosis -- multi-scale feature extraction -- convolutional neural network (CNN) -- domain adaptation; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/aca422
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: The intelligent rolling bearings fault diagnosis methods adopting a single vibration signal as the model input present low diagnostic precision, poor noise robustness, and difficulty in applying to variable operating conditions, so a multi-scale domain adaptation network (MSDAN) was put forward for variable load fault diagnosis of rolling bearings. This method combined multi-scale feature extraction with a lightweight convolutional neural network to extract complementary fault features from coarse-grained vibration signals at multiple time scales. Then, correlation alignment (CORAL) distance and domain identification adversarial learning were applied to extract domain invariant features to establish an end-to-end unsupervised fault diagnosis system for rolling bearings. The MSDAN model was evaluated using variable load-bearing datasets of two experimental setups and compared with other methods. The results show that MSDAN has better diagnostic accuracy and cross-domain adaptability than other domain adaptation fault diagnosis methods. In addition, our multi-scale method has more robust stability and generalization ability than any single-channel feature extraction method.