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State space models for condition monitoring: a case study

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  • Pedregal, Diego J.
  • Carmen Carnero, Ma

Abstract

A Condition Monitoring system can increase safety, quality and availability in industrial plants. Safety requirements are especially important in critical machineries, like a turbine driving a centrifugal compressor located at a petrochemical plant in the case study presented in this paper. A Condition Monitoring system is set up for vibration data coming from the turbine. Four years of monthly data observed at two different locations of the equipment are analysed. The core of the system is a model to forecast the state of the machine using data provided by the Condition Monitoring system at each moment in time. The model is based on the State Space framework whose associated recursive algorithms (Kalman Filter and Fixed Interval Smoothing) provide the basis for a number of different operations, from which the most important in the present context is the extrapolation of the distribution of forecasts on which the probability of failure is estimated. The cost model on which the decision of making a preventive replacement is taken is based on the ‘expected cost per unit time’ for a pre-determined critical value of the vibration measure. The system is thoroughly tested on the data.

Suggested Citation

  • Pedregal, Diego J. & Carmen Carnero, Ma, 2006. "State space models for condition monitoring: a case study," Reliability Engineering and System Safety, Elsevier, vol. 91(2), pages 171-180.
    • Handle: RePEc:eee:reensy:v:91:y:2006:i:2:p:171-180
    DOI: 10.1016/j.ress.2004.12.001
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    References listed on IDEAS

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    Cited by:

    1. Pedregal, Diego J. & Carmen Carnero, Ma., 2009. "Vibration analysis diagnostics by continuous-time models: A case study," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 244-253.
    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. García, Fausto P. & Pedregal, Diego J. & Roberts, Clive, 2010. "Time series methods applied to failure prediction and detection," Reliability Engineering and System Safety, Elsevier, vol. 95(6), pages 698-703.
    4. Amo-Salas, M. & López-Fidalgo, J. & Pedregal, D.J., 2015. "Experimental designs for autoregressive models applied to industrial maintenance," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 87-94.
    5. Xu, Xin & Chen, Nan, 2017. "A state-space-based prognostics model for lithium-ion battery degradation," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 47-57.
    6. Fausto Pedro García Márquez & Diego J. Pedregal & Clive Roberts, 2015. "New methods for the condition monitoring of level crossings," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(5), pages 878-884, April.
    7. Ming-Yi You & Guang Meng, 2012. "A modularized framework for predictive maintenance scheduling," Journal of Risk and Reliability, , vol. 226(4), pages 380-391, August.
    8. Carnero, MaCarmen, 2006. "An evaluation system of the setting up of predictive maintenance programmes," Reliability Engineering and System Safety, Elsevier, vol. 91(8), pages 945-963.

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