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Applied RCM2 algorithms based on statistical methods

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The main purpose of this paper is to implement a system capable of detecting faults in railway point mechanisms. This is achieved by developing an algorithm that takes advantage of three empirical criteria simultaneously capable of detecting faults from records of measurements of force against time. The system is dynamic in several respects: the base reference data is computed using all the curves free from faults as they are encountered in the experimental data; the algorithm that uses the three criteria simultaneously may be applied in on-line situations as each new data point becomes available; and recursive algorithms are applied to filter noise from the raw data in an automatic way. Encouraging results are found in practice when the system is applied to a number of experiments carried out by an industrial sponsor.

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Author information

Correspondence to Fausto Pedro García Márquez.

Additional information

Fausto Pedro García Márquez received his B.Sc. degrees in technical engineering from University of Castilla-La Mancha (UCLM) in 1995; engineering from University of Murcia, Spain; management and business administration from UCLM, Spain in 2006. He received his M.Sc. degrees on safety and health from UCLM, and transport specialist at Polytechnic University of Madrid, Spain. He got the European Doctorate Degree with Cum Laudem calification from UCLM, Spain, in 2004. He had been an academic visitor in the Department of Mechanical Engineering at the University of Sheffield, UK from 2000 to 2004, spending short research periods between 2 and 5 months per year. Thereby he has done a research visit to the Systems Division of the Leeds School of Business, at the University of Colorado at Boulder, USA, and the University of Valencia, Spain in 2001. Moreover he has collaborated with the Universities of Antioquia and Nacional de Medellín, Colombia and, Piura and Catlica (Peru). He is assistant professor at UCLM, where he is doing a postdoctoral visit in Railway Research Centre at Birmingham University, UK, from 2005 to 2007.

He has published more than 20 papers on journals and conferences in recent years. His research interests include fault prediction/detection, condition monitoring, life cycle cost and maintenance.

Diego J. Pedregal graduated from Autóma University of Madrid (UAM), Spain in 1991. He received his M. Sc. degree from the Spanish Fiscal Studies Institute in 1992 and the Ph. D. degree from the UAM in 1995. He spent three years in the United Kingdom in an associate research position, from 1994 to 1997. He is currently an associate professor at the Industrial Engineering School of Castilla-La Mancha University, Spain.

He has published more than 40 papers on journals and conferences in recent years. His research interests include econometrics and statistics in general, especially qualitative response models of any kind for the prediction of personal and corporate bankruptcy, time series analysis (virtually everything), but especially classical linear modelling, from the simplest to the most complex approaches, any type of univariate forecasting procedures (linear or nonlinear), recursive approaches to time series analysis, identification and estimation of nonlinear dynamic systems, signal extraction and seasonal adjustment, and data-based modelling and forecasting of linear and nonlinear systems.

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Márquez, F.P.G., Pedregal, D.J. Applied RCM2 algorithms based on statistical methods. Int J Automat Comput 4, 109–116 (2007). https://doi.org/10.1007/s11633-007-0109-1

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  • Maintenance
  • railways
  • state space models
  • system reliability
  • monitoring elements