RCM2 predictive maintenance of railway systems based on unobserved components models

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.