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A general model for life-cycle cost analysis of Condition-Based Maintenance enabled by PHM capabilities

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  • Compare, Michele
  • Antonello, Federico
  • Pinciroli, Luca
  • Zio, Enrico

Abstract

In this work, we propose a general modelling approach to estimate the life cycle cost of a system equipped with Prognostics and Health Management (PHM) capabilities, undergoing a Condition-Based Maintenance (CBM) policy. The approach builds on the Markov Chain theoretical framework, with transition probabilities linked to both PHM performance metrics of the literature and a novel metric. The developed approach can be used to guide economic decisions about CBM development, whichever the PHM algorithm is but provided that its performance metrics are estimated. The model is validated through a case study concerning a mechanical component of a train bogie affected by fatigue degradation, considering two different prognostic algorithms: Particle Filtering and a Model-Based approach.

Suggested Citation

  • Compare, Michele & Antonello, Federico & Pinciroli, Luca & Zio, Enrico, 2022. "A general model for life-cycle cost analysis of Condition-Based Maintenance enabled by PHM capabilities," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    • Handle: RePEc:eee:reensy:v:224:y:2022:i:c:s0951832022001600
    DOI: 10.1016/j.ress.2022.108499
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    References listed on IDEAS

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    2. Wang, Hongzhou, 2002. "A survey of maintenance policies of deteriorating systems," European Journal of Operational Research, Elsevier, vol. 139(3), pages 469-489, June.
    3. Compare, Michele & Bellani, Luca & Zio, Enrico, 2017. "Reliability model of a component equipped with PHM capabilities," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 4-11.
    4. Chiachío, Juan & Jalón, María L. & Chiachío, Manuel & Kolios, Athanasios, 2020. "A Markov chains prognostics framework for complex degradation processes," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    5. Zio, Enrico, 2022. "Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
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    8. Rocchetta, R. & Bellani, L. & Compare, M. & Zio, E. & Patelli, E., 2019. "A reinforcement learning framework for optimal operation and maintenance of power grids," Applied Energy, Elsevier, vol. 241(C), pages 291-301.
    9. Michele Compare & Luca Bellani & Enrico Zio, 2017. "Availability Model of a PHM-Equipped Component," Post-Print hal-01652232, HAL.
    10. Quintanar-Gago, David A. & Nelson, Pamela F. & Díaz-Sánchez, à ngeles & Boldrick, Michael S., 2021. "Assessment of steam turbine blade failure and damage mechanisms using a Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
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    12. Yoon, Joung Taek & Youn, Byeng D. & Yoo, Minji & Kim, Yunhan & Kim, Sooho, 2019. "Life-cycle maintenance cost analysis framework considering time-dependent false and missed alarms for fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 181-192.
    13. Mancuso, A. & Compare, M. & Salo, A. & Zio, E., 2021. "Optimal Prognostics and Health Management-driven inspection and maintenance strategies for industrial systems," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
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    Cited by:

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    2. He, Rui & Tian, Zhigang & Wang, Yifei & Zuo, Mingjian & Guo, Ziwei, 2023. "Condition-based maintenance optimization for multi-component systems considering prognostic information and degraded working efficiency," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    3. Pinciroli, Luca & Baraldi, Piero & Zio, Enrico, 2023. "Maintenance optimization in industry 4.0," Reliability Engineering and System Safety, Elsevier, vol. 234(C).

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