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Life-cycle maintenance cost analysis framework considering time-dependent false and missed alarms for fault diagnosis

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  • Yoon, Joung Taek
  • Youn, Byeng D.
  • Yoo, Minji
  • Kim, Yunhan
  • Kim, Sooho

Abstract

Fault diagnosis aims to diagnose system failures and to enable timely maintenance that, in turn, can minimize system maintenance costs. In order to evaluate and maximize the benefits from fault diagnosis, the life†cycle maintenance cost should be analyzed. This paper presents a framework for life†cycle maintenance cost analysis that considers time†dependent false and missed alarms in fault diagnosis. First, time†dependent false and missed alarms are proposed. The false and missed alarm rates are not static but vary depending on the health state of the engineered system, which changes over time. Second, a life†cycle maintenance cost analysis framework is proposed. This is based upon a stochastic simulation method that can incorporate time†dependent false and missed alarm rates and various uncertainties, such as health degradation and health restoration through maintenance. Third, a fault diagnosis model design method is proposed, based upon the proposed life†cycle maintenance cost analysis framework. The proposed method incorporates optimal false and missed alarm weights into the life†cycle maintenance cost. As a result, the proposed ideas enable accurate estimation and minimization of the overall life†cycle maintenance cost. The effectiveness of the proposed methods is demonstrated via a numerical example and an electro†hydrostatic actuator case study.

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  • 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.
    • Handle: RePEc:eee:reensy:v:184:y:2019:i:c:p:181-192
    DOI: 10.1016/j.ress.2018.06.006
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    References listed on IDEAS

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    Cited by:

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    2. Bode, Gerrit & Thul, Simon & Baranski, Marc & Müller, Dirk, 2020. "Real-world application of machine-learning-based fault detection trained with experimental data," Energy, Elsevier, vol. 198(C).
    3. Cláudia Ferreira & Ana Silva & Jorge de Brito & Ilídio S. Dias & Inês Flores-Colen, 2021. "Condition-Based Maintenance Strategies to Enhance the Durability of ETICS," Sustainability, MDPI, vol. 13(12), pages 1-18, June.
    4. Wang, Run-Zi & Gu, Hang-Hang & Zhu, Shun-Peng & Li, Kai-Shang & Wang, Ji & Wang, Xiao-Wei & Hideo, Miura & Zhang, Xian-Cheng & Tu, Shan-Tung, 2022. "A data-driven roadmap for creep-fatigue reliability assessment and its implementation in low-pressure turbine disk at elevated temperatures," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    5. 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).
    6. Zhang, Wei-Heng & Qin, Jianjun & Lu, Da-Gang & Liu, Min & Faber, Michael H., 2023. "Quantification of the value of condition monitoring system with time-varying monitoring performance in the context of risk-based inspection," Reliability Engineering and System Safety, Elsevier, vol. 231(C).

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