Yu Zang1, Wei Shangguan1,2, Baigen Cai1, Huasheng Wang1 and Michael G. Pecht3
1School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
2 State Key Laboratory of Rail Traffic Control and Safety, Beijing 100044, China
3 Center for Advanced Life Cycle Engineering, University of Maryland, College Park, MD 20742, USA
This paper develops a hybrid remaining useful life (RUL) prediction method and explores the feasibility for complex system equipment, using one of transmission equipment D-cables in high-speed railways as an example. RUL prediction is a promising way to reduce high maintenance costs for high-speed railways. However, there is no sufficient actual life-cycle data due to the lack of sensors, and no mature physics-of-failure model of the equipment in high-speed railways, which make it difficult to predict RUL. To solving this problem, firstly the failure modes, mechanisms, and effects of the D-cables are first analyzed, and accelerated life tests are run under different thermal stresses in Ansys to obtain the life-cycle data. Based on the life-cycle data, particle filtering (PF) method predicts the RUL based on Paris-Law model, meanwhile feedforward neural network (FNN) predicts the RUL under the same thermal stress with PF method, finally a hybrid RUL prediction method that combines model-based and data-driven methods is developed. The results are verified using simulation.