Track Irregularity Fault Identification Based on Evidence Reasoning Rule

Track irregularity fault commonly leads to the abnormal vibration of train which is the main cause of poor ride and even derailment. Based on Evidence Reasoning (ER) rule, this paper presents a fault identification method for determining the dynamic management levels of track irregularity using sample data measured from the accelerometers mounted in the axle-box and car-body of in-service train. Firstly, statistical approach of the sample casting is given to generate diagnosis evidence. Secondly, the ER rule is used to combine the diagnosis evidence coming from two accelerometers. The estimated irregularity displacement can obtained from the combined results. Thirdly, the dynamic levels of track irregularity can be identified according to the estimated displacement and the corresponding management standard. Finally, a representative experiment in Chinese railway line shows the superior identification accuracy of the proposed method by comparing with classical neural network-based approximating methodology.