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Use of dynamic Bayesian networks for life extension assessment of ageing systems

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  • Ramírez, Pedro A. Pérez
  • Utne, Ingrid Bouwer

Abstract

Extending the operating lifetime of ageing technical systems is of great interest for industrial applications. Life extension requires identifying and selecting decision alternatives which allow for a safe and economic operation of the system beyond its design lifetime. This article proposes a dynamic Bayesian network for assessing the life extension of ageing repairable systems. The main objective of the model is to provide decision support based on the system performance during a finite time horizon, which is defined by the life extension period. The model has three main applications: (i) assessing and selecting optimal decision alternatives for the life extension at present time, based on historical data; (ii) identifying and minimizing the factors that have a negative impact on the system performance; and (iii) reassessing and optimizing the decision alternatives during operation throughout the life extension period, based on updating the model with new operational data gathered. A case study illustrates the application of the model for life extension of a real firewater pump system in an oil and gas facility. The case study analyzes three decision alternatives, where preventive maintenance and functional test policies are optimized, and the uncertainty involved in each alternative is computed.

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  • Ramírez, Pedro A. Pérez & Utne, Ingrid Bouwer, 2015. "Use of dynamic Bayesian networks for life extension assessment of ageing systems," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 119-136.
  • Handle: RePEc:eee:reensy:v:133:y:2015:i:c:p:119-136
    DOI: 10.1016/j.ress.2014.09.002
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    1. Kijima, Masaaki & Morimura, Hidenori & Suzuki, Yasusuke, 1988. "Periodical replacement problem without assuming minimal repair," European Journal of Operational Research, Elsevier, vol. 37(2), pages 194-203, November.
    2. Jones, B. & Jenkinson, I. & Yang, Z. & Wang, J., 2010. "The use of Bayesian network modelling for maintenance planning in a manufacturing industry," Reliability Engineering and System Safety, Elsevier, vol. 95(3), pages 267-277.
    3. Marquez, David & Neil, Martin & Fenton, Norman, 2010. "Improved reliability modeling using Bayesian networks and dynamic discretization," Reliability Engineering and System Safety, Elsevier, vol. 95(4), pages 412-425.
    4. Pérez Ramírez, Pedro A. & Utne, Ingrid Bouwer, 2013. "Decision support for life extension of technical systems through virtual age modelling," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 55-69.
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