The Dynamic Fault Tree Rare Event Simulator

Budde, Carlos E.; Ruijters, Enno Jozef Johannes; Stoelinga, Mariëlle

doi:10.1007/978-3-030-59854-9_17

Cited by 8 publications

(4 citation statements)

References 19 publications

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“…SBIP 2.0 [19] implements an equivalent (semiautomatic, property-based) engine for dtmcs, while offering a richer set of temporal logics to define the property query in. Cosmos [9] and [24] implement importance sampling on Markov chains, the latter specialising in systems described as repairable Dynamic Fault Trees (dfts). All these tools can operate directly on Markovian models, and none offers fully automated isplit.…”

Section: Related Workmentioning

confidence: 99%

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Budde

2022

SIGMETRICS Perform. Eval. Rev.

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This work presents version 1.3 of the Finite Improbability Generator (FIG): a statistical model checker to estimate transient and steady-state reachability properties in stochastic automata. Specialised in rare event simulation via importance splitting, FIG implements RESTART and Fixed Effort algorithms. Its distinctive feature is push-button automation: users need not define an importance function, because FIG can derive it from the property query and model specifications. The input models are Input/Output Stochastic Automata with Urgency, written either in the native IOSA syntax or in the JANI exchange format. The theory backing FIG has demonstrated good efficiency, comparable to optimal importance splitting implemented ad hoc for specific systems. Written in C++, FIG is FOSS released under the GPLv3 in https://git.cs.famaf.unc.edu.ar/dsg/fig.

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Section: Related Workmentioning

confidence: 99%

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Budde

2022

SIGMETRICS Perform. Eval. Rev.

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“…The complete DFT can be analyzed with off-the-shelf DFT analysis tools, such as Storm [59] or DFTRES [11]. In our approach, we use the framework of Storm, which performs probabilistic model checking [2,40].…”

Section: Introductionmentioning

confidence: 99%

DFT modeling approach for operational risk assessment of railway infrastructure

Weik

Volk

Katoen

et al. 2022

Int J Softw Tools Technol Transfer

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Reliability engineering of railway infrastructure aims to understand failure processes and to improve the efficiency and effectiveness of investments and maintenance planning such that a high quality of service is achieved. While formal methods are widely used to verify the design specifications of safety-critical components in train control, quantitative methods to analyze the service reliability associated with specific system designs are only starting to emerge. In this paper, we strive to advance the use of formal fault-tree modeling for providing a quantitative assessment of the railway infrastructure’s service reliability in the design phase. While, individually, most subsystems required for route-setting and train control are well understood, the system’s reliability to globally provide its designated service capacity is less studied. To this end, we present a framework based on dynamic fault trees that allows to analyze train routability based on train paths projected in the interlocking system. We particularly focus on the dependency of train paths on track-based assets such as switches and crossings, which are particularly prone to failures due to their being subject to weather and heavy wear. By using probabilistic model checking to analyze and verify the reliability of feasible route sets for scheduled train lines, performance metrics for reliability analysis of the system as a whole as well as criticality analysis of individual (sub-)components become available. The approach, which has been previously discussed in our paper at FMICS 2019, is further refined, and additional algorithmic approaches, analysis settings and application scenarios in infrastructure and maintenance planning are discussed.

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“…For DFTs, various analysis techniques exist. Common approaches translate DFTs into models such as Markov models [21,9,55], Bayesian networks [37] and Petri nets [38], or are based on Monte-Carlo simulation [40,14]. There also exist analysis techniques based on extensions of BDDs such as sequence decision diagrams [43], sequential BDDs [57], multiple-valued decision diagrams [35] or conditional BDDs [59].…”

Section: Introductionmentioning

confidence: 99%

Artifact for "BDDs Strike Back - Efficient Analysis of Static and Dynamic Fault Trees"

Basgöze,

Volk,

Katoen

et al. 2022

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Fault trees are a key model in reliability analysis. Classical static fault trees (SFT) can best be analysed using binary decision diagrams (BDD). State-based techniques are favorable for the more expressive dynamic fault trees (DFT). This paper combines the best of both worlds by following Dugan's approach: dynamic sub-trees are analysed via model checking Markov models and replaced by basic events capturing the obtained failure probabilities. The resulting SFT is then analysed via BDDs. We implemented this approach in the Storm model checker. Extensive experiments (a) compare our pure BDD-based analysis of SFTs to various existing SFT analysis tools, (b) indicate the benefits of our efficient calculations for multiple time points and the assessment of the mean-time-to-failure, and (c) show that our implementation of Dugan's approach significantly outperforms pure Markovian analysis of DFTs. Our implementation Storm-dft is currently the only tool supporting efficient analysis for both SFTs and DFTs. This work has been partially funded by NWO under the grant PrimaVera number NWA.1160.18.238. Khan is funded by a HEC-DAAD stipend.

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The Dynamic Fault Tree Rare Event Simulator

Cited by 8 publications

References 19 publications

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DFT modeling approach for operational risk assessment of railway infrastructure

Artifact for "BDDs Strike Back - Efficient Analysis of Static and Dynamic Fault Trees"

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