PFL: A Probabilistic Logic for Fault Trees
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
; 14000 LNCS:199-221, 2023.
Article
in English
| Scopus | ID: covidwho-2300924
ABSTRACT
Safety-critical infrastructures must operate in a safe and reliable way. Fault tree analysis is a widespread method used for risk assessment of these systems fault trees (FTs) are required by, e.g., the Federal Aviation Administration and the Nuclear Regulatory Commission. In spite of their popularity, little work has been done on formulating structural queries about and analyzing these, e.g., when evaluating potential scenarios, and to give practitioners instruments to formulate queries on in an understandable yet powerful way. In this paper, we aim to fill this gap by extending [37], a logic that reasons about Boolean. To do so, we introduce a Probabilistic Fault tree Logic is a simple, yet expressive logic that supports easier formulation of complex scenarios and specification of FT properties that comprise probabilities. Alongside, we present, a domain specific language to further ease property specification. We showcase and by applying them to a COVID-19 related FT and to a FT for an oil/gas pipeline. Finally, we present theory and model checking algorithms based on binary decision diagrams (BDDs). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Computer circuits; Decision theory; Fault tree analysis; Model checking; Probabilistic logics; Problem oriented languages; Risk assessment; Safety engineering; Specifications; Fault tree analyses (FTA); Fault-trees; Federal Aviation Administration; Nuclear regulatory commission; Probabilistics; Risks assessments; Simple++; Structural query; System faults; Tree logic; Binary decision diagrams
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Year:
2023
Document Type:
Article
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