A Deep Learning-based Remaining Useful Life Prediction Approach for Engineering Systems

Abstract

As modern engineering systems tend to be more complicated and integrated, prognostics and health management (PHM) has become an important and practical technique to predict remaining useful life (RUL) and ensure reliability. However, there are still challenges for RUL prediction via multi-sensor data. Therefore, this paper focuses on the RUL prediction problem for complex engineering systems. In order to make full use of multi-sensor monitoring data and enhance prediction accuracy, a feature selection method is developed based on Relief algorithm, then a practical RUL prediction approach is proposed based on deep long and short term memory (LSTM) network in this paper. The proposed approach can selectively focus on certain important historical data without any prior knowledge. Experimental results and comparison with other conventional methods are presented to demonstrate the effectiveness and superiority of the proposed RUL prediction approach.