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A health performance prediction method of large-scale stochastic linear hybrid systems with small failure probability

Author

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  • Zhao, Zhiyao
  • Quan, Quan
  • Cai, Kai-Yuan

Abstract

Health performance prediction of a dynamical system aims at determining the probability or possibility that the system state will remain in a permitted area (safe set) or reach a forbidden area (unsafe set) at a future time instance. This paper proposes a health performance prediction algorithm for large-scale Stochastic Linear Hybrid Systems (SLHS) with small failure probability. In the studied SLHS, the continuous variable evolution is described by a set of stochastic linear differential equations, and the discrete state evolution is modeled by a first-order Markov chain. Furthermore, a safe set of the SLHS is described by a permitted area in the hybrid state space. Given an initial condition, a hybrid state evolution algorithm is proposed referring to the execution of stochastic hybrid systems. On this basis, a concept of health degree is introduced to evaluate the health performance of the studied SLHS. Finally, a multicopter with sensor anomalies is studied to validate the availability and effectiveness of the proposed method.

Suggested Citation

  • Zhao, Zhiyao & Quan, Quan & Cai, Kai-Yuan, 2017. "A health performance prediction method of large-scale stochastic linear hybrid systems with small failure probability," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 74-88.
    • Handle: RePEc:eee:reensy:v:165:y:2017:i:c:p:74-88
    DOI: 10.1016/j.ress.2017.03.014
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    References listed on IDEAS

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    1. Babykina, Génia & Brînzei, Nicolae & Aubry, Jean-François & Deleuze, Gilles, 2016. "Modeling and simulation of a controlled steam generator in the context of dynamic reliability using a Stochastic Hybrid Automaton," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 115-136.
    2. 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.
    3. Turati, Pietro & Pedroni, Nicola & Zio, Enrico, 2016. "Advanced RESTART method for the estimation of the probability of failure of highly reliable hybrid dynamic systems," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 117-126.
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