Probabilistic Fault Diagnosis of Safety Instrumented Systems based on Fault Tree Analysis and Bayesian Network
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TECHNICAL ARTICLE—PEER-REVIEWED
Probabilistic Fault Diagnosis of Safety Instrumented Systems based on Fault Tree Analysis and Bayesian Network Zakarya Chiremsel . Rachid Nait Said . Rachid Chiremsel
Submitted: 14 May 2016 / in revised form: 20 June 2016 Ó ASM International 2016
Abstract Safety instrumented systems (SISs) are used in the oil and gas industry to detect the onset of hazardous events and/or to mitigate their consequences to humans, assets, and environment. A relevant problem concerning these systems is failure diagnosis. Diagnostic procedures are then required to determine the most probable source of undetected dangerous failures that prevent the system to perform its function. This paper presents a probabilistic fault diagnosis approach of SIS. This is a hybrid approach based on fault tree analysis (FTA) and Bayesian network (BN). Indeed, the minimal cut sets as the potential sources of SIS failure were generated via qualitative analysis of FTA, while diagnosis importance factor of components was calculated by converting the standard FTA in an equivalent BN. The final objective is using diagnosis data to generate a diagnosis map that will be useful to guide repair actions. A diagnosis aid system is developed and implemented under SWI-Prolog tool to facilitate testing and diagnosing of SIS. Keywords SIS Fault tree Bayesian network Decision tree Model-based diagnosis Evidence Diagnostic importance factor
Z. Chiremsel (&) R. Nait Said IHSI-LRPI, University of Batna 2, Constantine road N° 53. Fesdis, Batna 05078, Algeria e-mail: [email protected] R. Nait Said e-mail: [email protected] R. Chiremsel Department of Computer Science, University of Batna 2, Constantine road N° 53. Fesdis, Batna 05078, Algeria
Introduction Safety instrumented systems (SISs), described by the standards IEC61508 and IEC61511, play an essential role in the accident prevention which can arise in the industrial systems [1, 2]. They go into action when the process is in abnormal conditions and that a dangerous situation causes high risks to develop. SISs are combinations of sensors, logic solvers, and actuators which are designed to fulfill safety functions. The unavailability of SISs compromises the safety of the global system. As a consequence, the failure analysis and diagnosis of SIS components regardless their technology or redundancy level turns out to be necessary [3, 4]. They allow detection and failure identification in an efficient and fast way. Hence, it would be possible to intervene only on the subsystem and/or defective components, so that repair time becomes shorter. Furthermore, diagnosis provides reliable and interpretable answer, in spite of the complexity of SIS [5]. The main tasks of diagnosis are the observation of the symptoms (evidence) based on logical reasoning. Diagnosis is required to detect failures in the SIS and its function may be active or passive. The active diagnosis is automatic, continuous, and able to detect failures before the integrity of the system is lost. It controls co
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