An unsupervised bearing fault diagnosis based on deep subdomain adaptation under noise and variable load condition

Deep learning-based approaches for diagnosing bearing faults have attracted considerable attention in the last years. However, in real-world applications, these methods face challenges. For proper training of these models, a considerable amount of labeled data are necessary, and due to limitations in industry, obtaining this amount of data may not be possible. Because of load variations, the distribution of training and test data may vary, which reduces the accuracy of the trained model for various working conditions. Furthermore, noise has a significant impact on bearing fault diagnosis performance in real-world industrial applications. This study introduced the deep subdomain adaptation convolutional neural network (DSACNN) method to overcome these challenges in real scenarios. The local maximum mean discrepancy (LMMD) method reduces the difference between each class distribution in the source and target domains. We validated our proposed method by CWRU bearing dataset under various loads and noise with different SNRs. The results show that DSACNN outperforms other comparative methods in anti-noise performance and reduction of domain distribution discrepancies.

Automated summary

Deep learning-based approaches for diagnosing bearing faults face challenges in real-world applications, such as the need for labeled data, distribution differences in training and test data, and noise impact. This study introduces the DSACNN method to overcome these challenges and validates its superiority in anti-noise performance and reduction of domain distribution discrepancies compared to other methods.