An unsupervised domain adaptation method with coral-maximum mean discrepancy for subway train transmission system fault diagnosis

Purpose In modern railway train systems, the safety of fault diagnosis technology is crucial. Currently, data-driven mainstream algorithms have achieved good results in fault diagnosis. However, current research typically focuses on the equipment data set itself, with training and test sets often being in the same distribution. Due to the diversity of equipment and operating conditions, applying these methods to industrial practice can be challenging. The purpose of this study is to address the challenges in applying data-driven fault diagnosis methods to industrial practice, given the diversity of railway train equipment and operating conditions. Design/methodology/approach To address this issue, the authors propose an unsupervised domain adaptation method for fault diagnosis in subway train transmission systems, which combines convolutional neural networks (CNN) and a novel domain adaptation algorithm, coral-maximum mean discrepancy (Cor-MMD). Findings First, feature extraction is performed on data sets of subway train transmission systems under various operating conditions, and the data sets are divided into training, validation and test sets. Next, the CNN is trained using the training set to obtain model weight files. Then, based on the weight files along with the labeled training set and unlabeled test set, a deep domain adaptation model is trained and the model is optimized by minimizing the Cor-MMD loss. Originality/value Finally, the accuracy of the model was tested on the test set, and the effectiveness of our method was verified through comparative experiments.