Railway turnout system RUL prediction based on feature fusion and genetic programming

Abstract

The remaining useful life (RUL) prediction of railway turnout systems (RTS) is very important to avoid unplanned shutdowns and reduce labor costs for the normal operation of railways. One key challenge on RUL prediction is how to construct an appropriate health indicator (HI) that can be utilized to infer conditions of RTS. Existing methods usually adopt some inherit merits (e.g., monotonicity, trendability, and robustness), and their prediction results lack real-world physical meaning due to their “black-box-like” property. In this paper, we present a novel feature fusion method for RUL prediction, which is able to capture the relationship between RUL and HI. A variant correlation-based feature selection method is utilized to extract features, which has the potential to depict the degradation process optimally, and then the selected features are fused by Auto-Associative Kernel Regression (AAKR) for prediction. To reduce the noise interference, the extracted features and the combined HI are all smoothed by using the locally weighted regression. Finally, a genetic programming (GP) algorithm is employed to predict the RUL of RTS. The proposed method is extensively tested on two turnout machine degradation datasets, and the results show that the proposed approach is effective for RUL prediction of RTS.