Title
A health performance prediction method of large-scale stochastic linear hybrid systems with small failure probability.
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.
Year
DOI
Venue
2017
10.1016/j.ress.2017.03.014
Reliability Engineering & System Safety
Keywords
Field
DocType
Health performance,Stochastic linear hybrid systems,Large-scale,Small failure probability,Multicopter
Mathematical optimization,Linear differential equation,Markov chain,Continuous variable,Initial value problem,Statistics,State space,Performance prediction,Hybrid system,Reliability engineering,Mathematics,Dynamical system
Journal
Volume
ISSN
Citations 
165
0951-8320
1
PageRank 
References 
Authors
0.36
23
3
Name
Order
Citations
PageRank
Zhiyao Zhao1165.45
Quan Quan211.71
Kai-Yuan Cai31332121.70