RCM2 predictive maintenance of railway systems based on unobserved components models

Abstract Turnouts are probably the most important infrastructure elements of the railway system because of its effect on the system safety, reliability and quality of the service. In this paper, a predictive maintenance system in point mechanism, called RCM2, has been implemented for increasing the quality service. RCM2 is based on the integration of the two other types of maintenance techniques, namely Reliability Centred Maintenance (RCM1) and Remote Condition Monitoring (RCM2). The core of the system consists of an Unobserved Components model set-up in a State Space framework, in which the unknown elements of the system are estimated by Maximum Likelihood. The detection of faults in the system is based on the correlation estimate between a curve free from faults (that is, continuously updated as new curves are incorporated in the data base) with the current curve data. If the correlation falls far from one, a fault is at hand. The detection system is tested on a set of 476 experiments carried out by the Universities of Sheffield and Castilla-La Mancha.

[1]  Arnold Zellner,et al.  Applied Time Series Analysis of Economic Data. , 1985 .

[2]  Michael P. Clements,et al.  A companion to economic forecasting , 2004 .

[3]  Fausto Pedro García Márquez,et al.  Wear assessment employing remote condition monitoring: a case study , 2003 .

[4]  J. Moubray.  Reliability-centred maintenance , 1995 .

[5]  Peter C. Young,et al.  Recursive Estimation and Time Series Analysis , 1984 .

[6]  P. Young,et al.  Dynamic harmonic regression. , 1999 .

[7]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[8]  S. Koopman,et al.  Disturbance smoother for state space models , 1993 .

[9]  Andrew Harvey,et al.  Forecasting, Structural Time Series Models and the Kalman Filter , 1990 .

[10]  Fred C. Schweppe,et al.  Evaluation of likelihood functions for Gaussian signals , 1965, IEEE Trans. Inf. Theory.

[11]  Siem Jan Koopman,et al.  Time Series Analysis by State Space Methods , 2001 .

[12]  Clive Roberts,et al.  Distributed quantitative and qualitative fault diagnosis: railway junction case study , 2002 .

[13]  Simon Brown,et al.  Overview of IEC 61508. Design of electrical/electronic/programmable electronic safety-related systems , 2000 .

[14]  Felix Schmid,et al.  A reliability centered approach to remote condition monitoring. A railway points case study , 2003, Reliab. Eng. Syst. Saf..

[15]  M. West,et al.  Bayesian forecasting and dynamic models , 1989 .

[16]  W. Wang.  A model to determine the optimal critical level and the monitoring intervals in condition-based maintenance , 2000 .

[17]  Miguel Jerez,et al.  Exact smoothing for stationary and non-stationary time series , 2000 .

[18]  R. Kohn,et al.  A fast algorithm for signal extraction, influence and cross-validation in state space models , 1989 .