Fault Diagnosis Based on 1D-CNN with Associated Auxiliary Tasks Boosted for Wheelset Bearings of High-Speed Trains

Abstract

Fault diagnosis for wheelset bearings of highspeed trains (HSTs) is very important for the reliability of the train operation. In recent years, deep learning technology has been widely used for fault diagnosis of mechanical components. Most deep learning methods are optimized from the data set or network structure. However, most target tasks themselves are complicated, and the factors leading to any of its results are not limited to the two aspects mentioned above. In general, there are one or more other factors related to the target task that influence the outcome of the final results. This paper introduces the idea of multi-task learning into the one-dimensional convolutional neural network (1D-CNN) model, and explores the possibility of enhancing the learning ability of the model by using factors related to the target task as auxiliary tasks. The experiment proves that the auxiliary tasks associated with the target task can indeed enhance the learning ability of the target task. And compared with other five cutting-edge fault diagnosis methods, the proposed model also has very good performance for wheelset bearings of HSTs.