A path-based algorithm to evaluate asymptotic unavailability for large Markov models

Bouissou, Marc; Lefebvre, Yannick

doi:10.1109/rams.2002.981616

Cited by 20 publications

(10 citation statements)

References 2 publications

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“…We have also solved the Petri net with the tool FIGSEQ, which is based on sequence exploration and quantification of the Markov graph specified by the Petri net. FIGSEQ uses an analytical quantification of sequences leading to a specified set of states 4,8 , and is able to process any markovian model written in the FIGARO language. FIGSEQ instantly solved the model and gave the following result for the probability of mission success: p = 0.92394.…”

Section: Compared Resultsmentioning

confidence: 99%

Reliability Analysis of a Dynamic Phased Mission System: Comparison of Two Approaches

Bouissou¹,

Dutuit²

2005

Series on Quality, Reliability and Engineering Statistics

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Phased mission systems are frequently encountered in industrial fields, and many approaches have been proposed in the literature to compute their reliability. After a short review of the existing literature, this paper aims to illustrate the use of two reliability analysis methods applied to a simple, but not trivial, problem. The system proposed as a testcase enables us to compare the respective benefits and drawbacks of a Petri net-based approach and of the so-called BDMP approach, recently published.

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Section: Compared Resultsmentioning

confidence: 99%

Reliability Analysis of a Dynamic Phased Mission System: Comparison of Two Approaches

Bouissou¹,

Dutuit²

2005

Series on Quality, Reliability and Engineering Statistics

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“…For repairable Markov processes it is possible to improve the efficiency of the simulations based on the asymptotic unavailability of the model [70] and this can be applied to the BDMP models in Fig. 12.…”

Section: B Simulation Timementioning

confidence: 99%

Improved Dynamic Dependability Assessment Through Integration With Prognostics

et al. 2017

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This version is available at https://strathprints.strath.ac.uk/60422/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. Abstract-The use of average data for dependability assessments results in a outdated system-level dependability estimation which can lead to incorrect design decisions. With increasing availability of online data, there is room to improve traditional dependability assessment techniques. Namely, prognostics is an emerging field which provides asset-specific failure information which can be reused to improve the system level failure estimation. This paper presents a framework for prognosticsupdated dynamic dependability assessment. The dynamic behaviour comes from runtime updated information, asset interdependencies, and time-dependent system behaviour. A case study from the power generation industry is analysed and results confirm the validity of the approach for improved near real-time unavailability estimations.

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“…[3,4,5]). The calculated quantity is obtained by summing-up individual values obtained for sequences.…”

Section: Related Workmentioning

confidence: 99%

“…The same idea is behind algorithms that consider failure sequences in turn, while keeping only probable enough sequences; see e.g. [3,4,5]. What we propose here is * Corresponding author rather to generate a relevant fraction of the whole Markov chain.…”

Section: Introductionmentioning

confidence: 99%

Automated generation of partial Markov chain from high level descriptions

Brameret¹,

Rauzy²,

Roussel³

2015

Reliability Engineering & System Safety

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International audienceWe propose an algorithm to generate partial Markov chains from high level implicit descriptions, namely AltaRica models. This algorithm relies on two components. First, a variation on Dijkstra's algorithm to compute shortest paths in a graph. Second, the definition of a notion of distance to select which states must be kept and which can be safely discarded. The proposed method solves two problems at once. First, it avoids a manual construction of Markov chains, which is both tedious and error prone. Second, up the price of acceptable approximations, it makes it possible to push back dramatically the exponential blow-up of the size of the resulting chains. We report experimental results that show the efficiency of the proposed approach

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A path-based algorithm to evaluate asymptotic unavailability for large Markov models

Cited by 20 publications

References 2 publications

Reliability Analysis of a Dynamic Phased Mission System: Comparison of Two Approaches

Reliability Analysis of a Dynamic Phased Mission System: Comparison of Two Approaches

Improved Dynamic Dependability Assessment Through Integration With Prognostics

Automated generation of partial Markov chain from high level descriptions

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