Fault detection of high-speed train axle bearings based on a hybridized physical and data-driven temperature model

Abstract

As a key component connecting axle and frame, axle bearings of the high-speed train are essential for the safe operation of modern high-speed trains. The current on-board fault detection system, which compares the axle box temperature measured from the sensor to a predefined threshold value, faces problems such as poor adaptability to varied service conditions, undesired high rate of false alarms and missing alarms, and lack of potential to be optimized. To solve these problems, in the present study, a novel hybrid model is proposed which predicts the temperature of a healthy bearing under the given service condition, and based on that detects the bearing fault. The hybrid model includes a physical model reflecting the thermal behavior of the axle box and two data-driven models adopting the same inputs but representing the relationship by means of back-propagation (BP) neural network and long-short term memory neural network (LSTM). After calibrating these models by real-life operation data collected from 2.25 million kilometers and using all results as inputs, a fault discriminant mechanism is established. The performance of this new mechanism is superior than the current on-board system and all stand-alone models in terms of indicators such as precision, recall and F1 score. This way, the study shows that similar hybrid models can be a viable solution for the future on-board fault detection system.