1986•IEEE Transactions on ReliabilityRequires access

Multistate Markov Models and Structural Properties of the Transition-Rate Matrix

Giuseppe Cafaro, Francesco Corsi, Francesco Vacca

Open publisher page 38 citations

Abstract

This paper explains how to use Markov models for evaluating the availability and reliability of a system when the transition rates of each component depend on the state of the system. To this purpose, the structural properties of the system transition-rate matrix, obtained by the Kronecker sum of transition rate matrices of the components, are analyzed. As a result, an efficient use of the Markov model is possible by mapping the transition rates of the single components into the elements of the system transition-rate matrix. The potential of parallel computers can facilitate this approach, giving renewed interest to the Markov method for complex systems.

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What this paper is about

This paper explains how to use Markov models for evaluating the availability and reliability of a system when the transition rates of each component depend on the state of the system. To this purpose, the structural properties of the system transition-rate matrix, obtained by the Kronecker sum of transition rate matrices of the components, are analyzed. As a result, an efficient use of the Markov model is possible by mapping the transition rates of the single components into the elements of the system transition-rate matrix. The potential of parallel computers can facilitate this approach, giving renewed interest to the Markov method for complex systems.

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OpenAlex reports 38 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

This paper explains how to use Markov models for evaluating the availability and reliability of a system when the transition rates of each component depend on the state of the system. To this purpose, the structural properties of the system transition-rate matrix, obtained by the Kronecker sum of transition rate matrices of the components, are analyzed. As a result, an efficient use of the Markov model is possible by mapping the transition rates of the single components into the elements of the system transition-rate matrix. The potential of parallel computers can facilitate this approach, giving renewed interest to the Markov method for complex systems.

Key concepts: Markov chain, Transition rate matrix, Stochastic matrix, Markov model, Markov process, Continuous-time Markov chain, Markov kernel, Kronecker delta

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