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

Zhiyao Zhao, Quan Quan and Kai-Yuan Cai

Reliability Engineering and System Safety, 2017, vol. 165, issue C, 74-88

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.

Keywords: Health performance; Stochastic linear hybrid systems; Large-scale; Small failure probability; Multicopter (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:165:y:2017:i:c:p:74-88

DOI: 10.1016/j.ress.2017.03.014

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