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An algorithmic approach for maintenance management based on advanced state space systems and harmonic regressions

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  • Diego Pedregal
  • Fausto García
  • Clive Roberts

Abstract

Point mechanisms are special track elements which failures results in delays and increased operating costs. In some cases such failures cause fatalities. A new robust algorithm for fault detection of point mechanisms is developed. It detects faults by comparing what can be considered the ‘normal’ or ‘expected’ shape of some signal with respect to the actual shape observed as new data become available. The expected shape is computed as a forecast of a combination of models. The proposed system deals with complicated features of the data in the case study, the main ones being the irregular sampling interval of the data and the time varying nature of the periodic behaviour. The system models are set up in a continuous-time framework and the system has been tested on a large dataset taken from a point mechanism operating on a commercial line. Copyright Springer Science+Business Media, LLC 2009

Suggested Citation

  • Diego Pedregal & Fausto García & Clive Roberts, 2009. "An algorithmic approach for maintenance management based on advanced state space systems and harmonic regressions," Annals of Operations Research, Springer, vol. 166(1), pages 109-124, February.
    • Handle: RePEc:spr:annopr:v:166:y:2009:i:1:p:109-124:10.1007/s10479-008-0403-5
    DOI: 10.1007/s10479-008-0403-5
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    References listed on IDEAS

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    1. Garcia Marquez, Fausto Pedro & Pedregal Tercero, Diego Jose & Schmid, Felix, 2007. "Unobserved Component models applied to the assessment of wear in railway points: A case study," European Journal of Operational Research, Elsevier, vol. 176(3), pages 1703-1712, February.
    2. Christer, A. H. & Wang, W. & Sharp, J. M., 1997. "A state space condition monitoring model for furnace erosion prediction and replacement," European Journal of Operational Research, Elsevier, vol. 101(1), pages 1-14, August.
    3. Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
    4. García Márquez, Fausto Pedro & Schmid, Felix, 2007. "A digital filter-based approach to the remote condition monitoring of railway turnouts," Reliability Engineering and System Safety, Elsevier, vol. 92(6), pages 830-840.
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    Cited by:

    1. Carlos Quiterio Gómez Muñoz & Fausto Pedro García Márquez, 2016. "A New Fault Location Approach for Acoustic Emission Techniques in Wind Turbines," Energies, MDPI, vol. 9(1), pages 1-14, January.
    2. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    3. Fausto Pedro García Márquez, 2022. "Advanced Analytics in Renewable Energy," Energies, MDPI, vol. 15(10), pages 1-5, May.
    4. Fausto Pedro García Marquez & Carlos Quiterio Gómez Muñoz, 2020. "A New Approach for Fault Detection, Location and Diagnosis by Ultrasonic Testing," Energies, MDPI, vol. 13(5), pages 1-13, March.
    5. Huerta Herraiz, Álvaro & Pliego Marugán, Alberto & García Márquez, Fausto Pedro, 2020. "Photovoltaic plant condition monitoring using thermal images analysis by convolutional neural network-based structure," Renewable Energy, Elsevier, vol. 153(C), pages 334-348.
    6. García Márquez, Fausto Pedro & Peco Chacón, Ana María, 2020. "A review of non-destructive testing on wind turbines blades," Renewable Energy, Elsevier, vol. 161(C), pages 998-1010.

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