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Diagnostic and prognostic of hybrid dynamic systems: Modeling and RUL evaluation for two maintenance policies

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  • Belkacem, Lobna
  • Simeu-Abazi, Zineb
  • Dhouibi, Hedi
  • Gascard, Eric
  • Messaoud, Hassani

Abstract

In the industrial sector, maintenance plays a very important role in carrying out production by increasing system reliability and availability. Thee maintenance decision is based primarily on diagnostic modules, prognostics and decision support. Diagnostic consists of detection and isolation of faults, while prognostic consists of prediction of the remaining useful life of systems. Moreover, recent industrial systems are naturally hybrid: their dynamic behavior is both continuous and discrete. This paper presents an integrating architecture of diagnostic and prognostic in a hybrid dynamic system. Indeed, the diagnostic system is based on controlling task execution times during system operation. This method is based on a general modeling approach using hybrid automata. The model proposed is detailed by studying a two-tank system. To validate the model, a Stateflow controller is used. These failures are anticipated by a prognostics process based on a prediction of the remaining life for each component by taking maintenance policy into account. Two new methods are compared: ABAO (As Bad As Old) and AGAN (As Good As New), based on the type of repair strategy.

Suggested Citation

  • Belkacem, Lobna & Simeu-Abazi, Zineb & Dhouibi, Hedi & Gascard, Eric & Messaoud, Hassani, 2017. "Diagnostic and prognostic of hybrid dynamic systems: Modeling and RUL evaluation for two maintenance policies," Reliability Engineering and System Safety, Elsevier, vol. 164(C), pages 98-109.
    • Handle: RePEc:eee:reensy:v:164:y:2017:i:c:p:98-109
    DOI: 10.1016/j.ress.2017.03.008
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    References listed on IDEAS

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    1. Hai-Kun Wang & Yan-Feng Li & Yu Liu & Yuan-Jian Yang & Hong-Zhong Huang, 2015. "Remaining useful life estimation under degradation and shock damage," Journal of Risk and Reliability, , vol. 229(3), pages 200-208, June.
    2. Traore, M. & Chammas, A. & Duviella, E., 2015. "Supervision and prognosis architecture based on dynamical classification method for the predictive maintenance of dynamical evolving systems," Reliability Engineering and System Safety, Elsevier, vol. 136(C), pages 120-131.
    3. Zhang, Mimi & Gaudoin, Olivier & Xie, Min, 2015. "Degradation-based maintenance decision using stochastic filtering for systems under imperfect maintenance," European Journal of Operational Research, Elsevier, vol. 245(2), pages 531-541.
    4. Al-Dahidi, Sameer & Di Maio, Francesco & Baraldi, Piero & Zio, Enrico, 2016. "Remaining useful life estimation in heterogeneous fleets working under variable operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 109-124.
    5. Khorasgani, Hamed & Biswas, Gautam & Sankararaman, Shankar, 2016. "Methodologies for system-level remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 8-18.
    6. 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.
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    Cited by:

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    2. Soleimani, Morteza & Campean, Felician & Neagu, Daniel, 2021. "Integration of Hidden Markov Modelling and Bayesian Network for fault detection and prediction of complex engineered systems," Reliability Engineering and System Safety, Elsevier, vol. 215(C).

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