Track and Vehicle Condition Monitoring during Normal Operation Using Reduced Sensor Sets

Abstract

The Enhanced Rail Contribution by Increased Reliability (ERCIR) project aimed to enhance availability by condition monitoring of the track and vehicle suspension from in-service trains. In this project, the possibility of instrumenting an inservice train is explored, with emphasis on using a minimal sensor set for the detection and diagnosis of track and vehicle faults. The use of a bogie-mounted pitch rate gyro to observe mean vertical track geometry is novel, as is using a bogiemounted yaw rate gyro to observe mean lateral alignment as well as the more usual longer wavelength track curvature. Mathematical algorithms are developed to detect vehicle suspension faults and track irregularities. The suspension faults considered are changes in secondary lateral and anti-yaw dampers, and changes in effective conicity. Sudden changes are detected and diagnosed using a Kalman filter-based innovation approach; gradual changes in damping coefficients and effective conicity are detected by parameter estimation using a Rao-Blackwellised particle filter. Observable track geometry irregularities include mean vertical and lateral alignment irregularities, as well as crosslevel and twist faults. This paper finally describes the results of trials carried out on two different railway vehicles, using the data obtained to assess the validity of the fault detection algorithms.