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Dynamic Risk Assessment for CBM-based adaptation of maintenance planning

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  • Martínez-Galán Fernández, Pablo
  • Guillén López, Antonio J.
  • Márquez, Adolfo Crespo
  • Gomez Fernández, Juan Fco.
  • Marcos, Jose Antonio

Abstract

This paper proposes a practical method for dynamic maintenance planning based on Dynamic Risk Assessment (DRA). This is founded on the interpretation, in terms of risk levels evolution, the available information of monitoring events and maintenance activities integrated in and that conform the condition-based maintenance (CBM) processes. DRA proposal is supported by ISO 31000 risk management framework in order to better understanding and results integration within other risk management approaches. Proposed method analyzes CBM results (monitoring events and maintenance activities) regarding their impact on failure risk level, and how to program and manage maintenance decision making (maintenance planning) regarding with dynamic risk evolution. This strategy not only helps maintenance management optimization but also facilitates the link of intelligent maintenance with global risk management within the organization, which is lined with modern Asset Management principles. To illustrate the method, an example of a real use case is presented where it is applied to the dynamic maintenance planning of a critical component in a high-speed train, and which integrates monitoring, predictive analytics and inspection data.

Suggested Citation

  • Martínez-Galán Fernández, Pablo & Guillén López, Antonio J. & Márquez, Adolfo Crespo & Gomez Fernández, Juan Fco. & Marcos, Jose Antonio, 2022. "Dynamic Risk Assessment for CBM-based adaptation of maintenance planning," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    • Handle: RePEc:eee:reensy:v:223:y:2022:i:c:s0951832022000382
    DOI: 10.1016/j.ress.2022.108359
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    Cited by:

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    2. Mandelli, Diego & Wang, Congjian & Agarwal, Vivek & Lin, Linyu & Manjunatha, Koushik A., 2024. "Reliability modeling in a predictive maintenance context: A margin-based approach," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    3. Xia, Liqiao & Liang, Yongshi & Leng, Jiewu & Zheng, Pai, 2023. "Maintenance planning recommendation of complex industrial equipment based on knowledge graph and graph neural network," Reliability Engineering and System Safety, Elsevier, vol. 232(C).

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