Machine learning based estimation of residual useful life of high-speed train wheels based on vehicle-mounted vibration sensor data

Safety is always a top priority during the development of high-speed rail systems. Wheel, as one of the most important components of a high-speed train, requires frequent inspections and maintenances in order to prevent operation failure of the train or even catastrophes from happening. The Residual Useful Life (RUL) has been extensively studied in the research field of health management for industrial components or systems. It is critical to understand the RUL of the high-speed train wheel for informed maintenance and replacement. Given an accurate estimation of train wheel RUL, rail companies can optimize maintenance and repair schedules more economically while ensuring safety. In this paper, we develop a machine learning based methodology to estimate the RUL of high-speed train wheels, using actual, in-field vibration data from sensors mounted on bogies. To our best knowledge, few studies have used data from in-field sensors mounted directly on train components to train and test a model for train wheel RUL estimation. A traditional time-domain signal processing method is implemented to extract characteristic features from the vibration data. Various machine learning models are introduced and applied to validate our proposed method. The estimation results conform to the empirical data, and can be used to infer the RUL of the wheel based on vehicle-mounted vibration sensor data for rail safety management.