An Online Health Monitoring Framework for Traction Motors in High-Speed Trains Using Temperature Signals

Abstract

The health monitoring of traction motors is crucial for the prognostics and health management of high-speed trains. The temperature signal is an outstanding health indicator. Due to the representing of the traction motor's health conditions and low cost, accurate prediction for the motor temperature is conducive to early detection of abnormalities. However, the traditional prediction models are trained offline with high dependency on training data and cannot adapt to varying distributions of real data timely. Therefore, over time, the accuracies of these models always decrease noticeably. Concerning this issue, we propose an online health monitoring framework for traction motors using temperature signals. First, in the offline phase, multisensor signals are utilized to develop a generalized prediction model to absorb extensive information from temperature and relevant signals. Second, during the online phase, the training parameters are dynamically estimated to fulfill individualized learning by adopting a combination of the sample complexity and real-time prediction errors so as to fulfill individualized training according to the monitored data samples. Furthermore, a low-regret strategy is also presented in the online phase to determine the optimization target of the model to make the online update adaptive enough to the online prediction task. Consequently, the model can obtain new knowledge and greater understanding about the real data by online-learning continuously. Finally, the proposed framework is verified by actual data collected from Chinese high-speed trains. Compared with the conventional multilayer perceptron, gated recurrent unit, and long short-term memory, new patterns of stream data can be captured and adapted by using our framework, and the average root mean square errors of prediction results are reduced by 5%, 12%, and 11%, the average mean absolute percentage errors are reduced by 10%, 12%, and 11%, respectively. It is proven that our framework has high prediction accuracy and well-performed adaptability on real datasets.