OORNet: A deep learning model for on-board condition monitoring and fault diagnosis of out-of-round wheels of high-speed trains

Abstract

The problem of train wheel out-of-roundness (OOR) negatively affects both humans and the vehicle-track system, incl. reduced passenger comfort, rapid aging of vehicle/track components, increase in derailment risk, etc. It is therefore of interest to develop an on-board condition monitoring and fault diagnosis (CM&FD) technique for wheel OOR, which contributes not only to the maintenance decision-making of wheelsets but also to clarifying its triggering and evolution mechanisms. This paper first shows how to express the problem of CM&FD of our-of-round wheels as a machine learning problem. A deep learning model, OORNet, is then developed for CM&FD of out-of-round wheels. A vehicle-track multi-body dynamics model of a China railway high-speed (CRH) trailer is meanwhile built to produce a database consisting of vertical axlebox vibration accelerations caused by 2000 different wheel OOR curves. The simulated database is finally used to test the performance of OORNet, and its feasibility and superiority are verified.