Analysis of non-Markovian repairable fault trees through rare event simulation

• Autores: Budde, Carlos E.; D'Argenio, Pedro Ruben; Monti, Raúl Enrique; Stoelinga, Mariëlle
• Año de publicación: 2022
• Idioma: inglés
• Tipo de recurso: artículo
• Fuente: ISSN 1433-2779; eISSN 1433-2787
• Materia: Fault tree analysis; Rare event simulation; Statistical model checking; System reliability; Análisis de árboles de fallas; Simulación de eventos raros; Confiabilidad de sistemas
• Institución: Universidad Nacional de Córdoba

Fil: D'Argenio, Pedro Ruben. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.
Fil: D'Argenio, Pedro Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: D'Argenio, Pedro Ruben. Saarland University. Department of Computer Science; Germany.
Fil: Budde, Carlos E. University of Trento. Department of Information Engineering and Computer; Italy.
Fil: Monti, Raúl Enrique. University of Twente; The Netherlands.
Fil: Stoelinga, Mariëlle. University of Twente; The Netherlands.
Fil: Stoelinga, Mariëlle. Radboud University. Department of Software Science; The Netherlands.
Dynamic fault trees (DFTs) are widely adopted in industry to assess the dependability of safety-critical equipment. Since many systems are too large to be studied numerically, DFTs dependability is often analysed using Monte Carlo simulation. A bottleneck here is that many simulation samples are required in the case of rare events, e.g. in highly reliable systems where components seldom fail. Rare event simulation (RES) provides techniques to reduce the number of samples in the case of rare events. In this article, we present a RES technique based on importance splitting to study failures in highly reliable DFTs, more precisely, on a variant of repairable fault trees (RFT). Whereas RES usually requires meta-information from an expert, our method is fully automatic. For this, we propose two different methods to derive the so-called importance function. On the one hand, we propose to cleverly exploit the RFT structure to compositionally construct such function. On the other hand, we explore different importance functions derived in different ways from the minimal cut sets of the tree, i.e., the minimal units that determine its failure. We handle RFTs with Markovian and non-Markovian failure and repair distributions—for which no numerical methods exist—and implement the techniques on a toolchain that includes the RES engine FIG, for which we also present improvements. We finally show the efficiency of our approach in several case studies.
This work was partially supported by the EU Grant Agreement 101008233 (MISSION), ANPCyT PICT-2017-3894 RAFTSys), and SeCyT project 33620180100354CB (ARES). Funded also by the EU Grant Agreement 101067199 (ProSVED).
info:eu-repo/semantics/publishedVersion
Fil: D'Argenio, Pedro Ruben. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.
Fil: D'Argenio, Pedro Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: D'Argenio, Pedro Ruben. Saarland University. Department of Computer Science; Germany.
Fil: Budde, Carlos E. University of Trento. Department of Information Engineering and Computer; Italy.
Fil: Monti, Raúl Enrique. University of Twente; The Netherlands.
Fil: Stoelinga, Mariëlle. University of Twente; The Netherlands.
Fil: Stoelinga, Mariëlle. Radboud University. Department of Software Science; The Netherlands.