Data-Driven Designs of Fault Identification via Collaborative Deep Learning for Traction Systems in High-Speed Trains

Abstract

Due to the advanced development of sensor technology, the data deluge has begun in the complex systems of high-speed trains (HSTs) and, therefore, hastens the popularity of data-driven research. Among these activities, data-driven detection and identification of faults have received considerable attention to ensure the safe and reliable operations of HST, especially the deep learning-based methods. Up to now, these deep learning-based methods are effective only for static systems. It, hence, motivates us to develop the data-driven fault identification (FI) method for traction systems in HST. In this study, we will develop an FI method via the collaborative deep learning method, where the first neural network is used for eliminating dynamic behaviors, and the second neural network is responsible for identifying the fault amplitude. By the use of the proposed neural networks with a deep architecture, the FI task can be achieved in a collaborative fashion. Its successful application on the traction systems of HST illustrates the effectiveness of collaborative deep learning on the one hand and opens an avenue on the data-driven FI methods using neural networks on the other hand.