A CNN-BiGRU Based Life Prediction Method for Rolling Pins of Rail Vehicle Door System

Abstract

As a key mechanical component in the door system of rail vehicles, the rolling pin is closely related to the safe operation of the door system. For the purpose of maintaining the safety of the door system of rail vehicles, it is necessary to accurately predict the Remaining Useful Life (RUL) of the rolling pin. Since the degree of wear is difficult to measure, it is quite hard to predict its life in real time. Synchronously, the amount of data that can characterize the life of the rolling pin is rarely available. To predict the RUL of rolling pin online as well as provide decision support for active maintenance, this paper proposes an RUL prediction method of rolling pin based on the Convolutional Neural Network (CNN) and Bi-directional Gated Recursive Unit (BiGRU), which combines the feature extraction ability of CNN and the information retention ability of BiGRU, enabling this model to be effective in dealing with several small sample issues. The simulation results demonstrate that such a method can accurately predict the life of the rolling pin, which has essential engineering application value.