Fuzzy evidence theory and Bayesian networks for process systems risk analysis

Abstract

Quantitative risk assessment (QRA) approaches systematically evaluate the likelihood, impacts and risk of adverse events. QRA using fault tree analysis (FTA) is based on the assumptions that failure events have crisp probabilities and they are statistically independent. The crisp probabilities of the events are often absent, which leads to data uncertainty. On the other hand, the independence assumption leads to model uncertainty. Experts' knowledge can be utilised to obtain unknown failure data; however, this process itself is subject to different issues such as imprecision, incompleteness, and lack of consensus. For this reason, to minimise the overall uncertainty in QRA, in addition to addressing the uncertainties in the knowledge, it is equally important to combine the opinions of multiple experts and update prior beliefs based on new evidence. In this paper, a novel methodology is proposed for QRA by combining fuzzy set theory and evidence theory with Bayesian networks to describe the uncertainties, aggregate experts' opinions, and update prior probabilities when new evidences become available. Additionally, sensitivity analysis is performed to identify the most critical events in the FTA. The effectiveness of the proposed approach has been demonstrated via application to a practical system.

Fuzzy smart failure modes and effects analysis to improve safety performance of system: Case study of an aircraft landing system

“…As mentioned in the literature, in order to cope with the ambiguity of FMEA based on experts' elicitation, fuzzy logic introduced by Zadeh can be used for this purpose. In earlier studies, triangular and trapezoidal fuzzy numbers have been widely used and recognized to be more effective for risk assessment purposes . Therefore, both triangular and trapezoidal fuzzy numbers were utilized to map linguistic terms to fuzzy membership functions.…”

“…In earlier studies, triangular and trapezoidal fuzzy numbers have been widely used and recognized to be more effective for risk assessment purposes. [39][40][41] Therefore, both triangular and trapezoidal fuzzy numbers were utilized to map linguistic terms to fuzzy membership functions. The reason for using these two types of fuzzy numbers is that under some weak assumptions, the defined membership functions can directly meet the appropriate optimization criteria.…”

Introducing a heuristic approach to enhance the reliability of system safety assessment

“…The reliability of objective probabilistic risk assessment methods are more than subjective ones, and reliability of both mentioned approaches are more than integration of objectivity and subjectivity in an approach. Therefore, it is required to do a probabilistic risk assessment merely based on objective data or subjective experts' opinions With consideration of Fact 1 and any safety management system policy, the probabilistic risk assessment techniques aim to reduce the probability of undesired event to an acceptable level.…”

“…The reliability of objective probabilistic risk assessment methods are more than subjective ones, and reliability of both mentioned approaches are more than integration of objectivity and subjectivity in an approach. Therefore, it is required to do a probabilistic risk assessment merely based on objective data or subjective experts' opinions …”

Ignorance‐aware safety and reliability analysis: A heuristic approach

“…DST is integrated with D numbers and TOPSIS to prioritize the cruciality of the failure modes on the basis of the degree of risk factors . DST and fuzzy set theory are combined to compute the probability of root events in the probabilistic risk assessment methods when it is hard to find the failure rate of the components . For a similar purpose, Khakzad combined DST with a Bayesian network to deal and manipulate the imprecise probabilities.…”