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An integrated framework for online diagnostic and prognostic health monitoring using a multistate deterioration process

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  • Moghaddass, Ramin
  • Zuo, Ming J.

Abstract

Efficient asset management is of paramount importance, particularly for systems with costly downtime and failure. As in energy and capital-intensive industries, the economic loss of downtime and failure is huge, the need for a low-cost and integrated health monitoring system has increased significantly over the years. Timely detection of faults and failures through an efficient prognostics and health management (PHM) framework can lead to appropriate maintenance actions to be scheduled proactively to avoid catastrophic failures and minimize the overall maintenance cost of the systems. This paper aims at practical challenges of online diagnostics and prognostics of mechanical systems under unobservable degradation. First, the elements of a multistate degradation structure are reviewed and then a model selection framework is introduced. Important dynamic performance measures are introduced, which can be used for online diagnostics and prognostics. The effectiveness of the result of this paper is demonstrated with a case study on the health monitoring of turbofan engines.

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  • Moghaddass, Ramin & Zuo, Ming J., 2014. "An integrated framework for online diagnostic and prognostic health monitoring using a multistate deterioration process," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 92-104.
    • Handle: RePEc:eee:reensy:v:124:y:2014:i:c:p:92-104
    DOI: 10.1016/j.ress.2013.11.006
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    9. Chen, Gaige & Chen, Jinglong & Zi, Yanyang & Miao, Huihui, 2017. "Hyper-parameter optimization based nonlinear multistate deterioration modeling for deterioration level assessment and remaining useful life prognostics," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 517-526.
    10. Zhiguo Zeng & Francesco Di Maio & Enrico Zio & Rui Kang, 2017. "A hierarchical decision-making framework for the assessment of the prediction capability of prognostic methods," Journal of Risk and Reliability, , vol. 231(1), pages 36-52, February.
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    14. Duan, Chaoqun & Makis, Viliam & Deng, Chao, 2020. "A two-level Bayesian early fault detection for mechanical equipment subject to dependent failure modes," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    15. Compare, Michele & Baraldi, Piero & Marelli, Paolo & Zio, Enrico, 2020. "Partially observable Markov decision processes for optimal operations of gas transmission networks," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    16. Zhang, Aibo & Wu, Shengnan & Fan, Dongming & Xie, Min & Cai, Baoping & Liu, Yiliu, 2022. "Adaptive testing policy for multi-state systems with application to the degrading final elements in safety-instrumented systems," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    17. Dui, Hongyan & Zhang, Chi & Bai, Guanghan & Chen, Liwei, 2021. "Mission reliability modeling of UAV swarm and its structure optimization based on importance measure," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    18. Eleftheroglou, Nick & Zarouchas, Dimitrios & Loutas, Theodoros & Alderliesten, Rene & Benedictus, Rinze, 2018. "Structural health monitoring data fusion for in-situ life prognosis of composite structures," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 40-54.
    19. Sameer Al-Dahidi & Francesco Di Maio & Piero Baraldi & Enrico Zio, 2017. "A locally adaptive ensemble approach for data-driven prognostics of heterogeneous fleets," Journal of Risk and Reliability, , vol. 231(4), pages 350-363, August.
    20. Zhao, Zeqi & Bin Liang, & Wang, Xueqian & Lu, Weining, 2017. "Remaining useful life prediction of aircraft engine based on degradation pattern learning," Reliability Engineering and System Safety, Elsevier, vol. 164(C), pages 74-83.
    21. Peng, Weiwen & Li, Yan-Feng & Mi, Jinhua & Yu, Le & Huang, Hong-Zhong, 2016. "Reliability of complex systems under dynamic conditions: A Bayesian multivariate degradation perspective," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 75-87.
    22. Malinowski, Simon & Chebel-Morello, Brigitte & Zerhouni, Noureddine, 2015. "Remaining useful life estimation based on discriminating shapelet extraction," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 279-288.
    23. Yuanju Qu & Zengtao Hou, 2022. "Degradation principle of machines influenced by maintenance," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1521-1530, June.
    24. Ying Liao & Yisha Xiang & Min Wang, 2021. "Health assessment and prognostics based on higher‐order hidden semi‐Markov models," Naval Research Logistics (NRL), John Wiley & Sons, vol. 68(2), pages 259-276, March.

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