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Quantifying the added value of an imperfectly performing condition monitoring system—Application to a wind turbine gearbox

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  • Van Horenbeek, Adriaan
  • Van Ostaeyen, Joris
  • Duflou, Joost R.
  • Pintelon, Liliane

Abstract

Implementation of a condition monitoring system is a difficult decision due to many uncertain parameters. This is certainly the case for the wind turbine industry where factors like long logistical times and weather conditions have a major influence on the economic benefit. One of the parameters that is neglected in most of the available literature is the performance of the condition monitoring system itself. In this paper a new concept for modeling this performance based on the P–F curve of different failure modes is presented. The concept is illustrated on an extensive case study for a gearbox of a wind turbine. A stochastic simulation model is constructed in order to quantify the economic added value of implementing an imperfectly performing condition monitoring system into a gearbox. This case study proves that a condition monitoring system generates an economic benefit compared to the currently applied maintenance strategy. However, the magnitude of this benefit depends strongly on the performance of the condition monitoring system.

Suggested Citation

  • Van Horenbeek, Adriaan & Van Ostaeyen, Joris & Duflou, Joost R. & Pintelon, Liliane, 2013. "Quantifying the added value of an imperfectly performing condition monitoring system—Application to a wind turbine gearbox," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 45-57.
    • Handle: RePEc:eee:reensy:v:111:y:2013:i:c:p:45-57
    DOI: 10.1016/j.ress.2012.10.010
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    References listed on IDEAS

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    1. Bedford, Tim & Dewan, Isha & Meilijson, Isaac & Zitrou, Athena, 2011. "The signal model: A model for competing risks of opportunistic maintenance," European Journal of Operational Research, Elsevier, vol. 214(3), pages 665-673, November.
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    4. Nielsen, Jannie Jessen & Sørensen, John Dalsgaard, 2011. "On risk-based operation and maintenance of offshore wind turbine components," Reliability Engineering and System Safety, Elsevier, vol. 96(1), pages 218-229.
    5. Hameed, Z. & Ahn, S.H. & Cho, Y.M., 2010. "Practical aspects of a condition monitoring system for a wind turbine with emphasis on its design, system architecture, testing and installation," Renewable Energy, Elsevier, vol. 35(5), pages 879-894.
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    Cited by:

    1. Izquierdo, J. & Márquez, A. Crespo & Uribetxebarria, J. & Erguido, A., 2020. "On the importance of assessing the operational context impact on maintenance management for life cycle cost of wind energy projects," Renewable Energy, Elsevier, vol. 153(C), pages 1100-1110.
    2. Zhang, Chen & Hu, Di & Yang, Tao, 2022. "Anomaly detection and diagnosis for wind turbines using long short-term memory-based stacked denoising autoencoders and XGBoost," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    3. Ahmed Raza & Vladimir Ulansky, 2019. "Optimal Preventive Maintenance of Wind Turbine Components with Imperfect Continuous Condition Monitoring," Energies, MDPI, vol. 12(19), pages 1-24, October.
    4. Artigao, Estefania & Martín-Martínez, Sergio & Honrubia-Escribano, Andrés & Gómez-Lázaro, Emilio, 2018. "Wind turbine reliability: A comprehensive review towards effective condition monitoring development," Applied Energy, Elsevier, vol. 228(C), pages 1569-1583.
    5. Godoy, David R. & Pascual, Rodrigo & Knights, Peter, 2013. "Critical spare parts ordering decisions using conditional reliability and stochastic lead time," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 199-206.
    6. Shafiee, Mahmood & Sørensen, John Dalsgaard, 2019. "Maintenance optimization and inspection planning of wind energy assets: Models, methods and strategies," Reliability Engineering and System Safety, Elsevier, vol. 192(C).

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