SAFETY ASSESSMENT FOR SAFETY-CRITICAL SYSTEMS USING MARKOV CHAIN MODULAR APPROACH
Yangyang Yu, B.W. Johnson
Abstract
Yangyang Yu, B.W. Johnson
Abstract
The Markov Chain Modular (MCM) approach is proposed in this paper in order to solve part of the failure-state dependency problem. The MCM approach completely avoids the failure-state dependency problem by avoiding the combinatorial modeling. To quantitatively assess safety, a new Markov chain modeling technique is developed to represent an m + 2 state homogenous Markov chain model using a three-state Markov model. The transition rate functions of the three-state Markov model can be determined by the transition rates of the m + 2 state Markov chain model. Given a series system has N modules and each module has O(m) operational states, the MCM approach reduces the operational states to O(N × m2) as opposed to O(m2N) by using the traditional Markov chain model.
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The Markov Chain Modular (MCM) approach is proposed in this paper in order to solve part of the failure-state dependency problem. The MCM approach completely avoids the failure-state dependency problem by avoiding the combinatorial modeling. To quantitatively assess safety, a new Markov chain modeling technique is developed to represent an m + 2 state homogenous Markov chain model using a three-state Markov model. The transition rate functions of the three-state Markov model can be determined by the transition rates of the m + 2 state Markov chain model. Given a series system has N modules and each module has O(m) operational states, the MCM approach reduces the operational states to O(N × m2) as opposed to O(m2N) by using the traditional Markov chain model.
Key concepts: Markov chain, Markov model, Dependency (UML), Computer science, Modular design, Additive Markov chain, Variable-order Markov model, Markov process