| The railway system is experimenting a deep transformation nowadays. The introduction of high speed networks and increased traffic levels require new technologies in railway infrastructure and trains, must go through a rigorous control of quality service and maintenance processes during their operative lives.; From an economic, quality and safety point of view, turnouts are certainly one of the most critical infrastructure elements in railway transportation. A predictive maintenance system called RCM2 has been implemented in point mechanism. RCM2 is based on the integration of the two other types of maintenance techniques, namely Reliability Centred Maintenance (RCM1) and Remote Condition Monitoring (RMC2).; The author has developed a model based on the criteria as follow: (a) Irregularities in the signal shape. (b) Deviation of maximum value position of the curves. (c) Signature symmetry with respect to the maximum value position. He demonstrates the approach using data from tests on a commonly found point mechanism and include a discussion of the benefits of adopting a Kalman Filter for pre-processing the data collected during tests. The Kalman Filter consists of estimating future states based to historic data and is employed in this work as a tool to filter the data that is being processed.; In order to improve the results obtained with the model above, the author employed a method which 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 in based in the correlation estimate between a curve free from faults (that is continuously updated as news curves are incorporated in the data base) with the current curve data. If the correlation falls far from one, a fault is at hand. |
