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

Abstract

Probabilistic risk assessment techniques are the important tools which can considerably improve the safety performance of the studied system and reduce the risk to an acceptable level. Typically, decision‐making process is an important part of risk assessment methods that accordingly bring the ambiguity inside. Decision makers as experts commonly express their subjective opinions about the occurrence of the root events in order to obtain the probability of the undesired event. Subsequently, the critical root events are identified, and possible intervention is performed to reduce the probability of the critical events. However, the serious point is the viability of the obtained probabilities and priority ranking of the critical events. In this study, a heuristic optimization model of linear mathematical programming using triangular intuitionistic fuzzy number (TrIFN) is proposed to obtain the feasible, optimum, and reliable results compared with available methods. The Spearman correlation is performed to examine the reliability and behavior of the proposed model. In order to show the effectiveness of the proposed approach, it is applied on a real case study. The application of the model confirms its robustness to prioritize critical root events over the conventional one.

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

“…Failure modes (FMs) and effects analysis (FMEA) is a structural risk assessment technique that started in the aerospace industry in the 1960s. FMEA is widely used to define, identify, and eliminate potential failures; and because the risk assessment is a continuous procedure, process designs are often better improved . According to some evidence‐based experts' opinions from the FMEA team members, each FM will obtain risk priority number (RPN) to represent the level of its risk.…”

“…FMEA is widely used to define, identify, and eliminate potential failures; and because the risk assessment is a continuous procedure, process designs are often better improved. [1][2][3][4][5][6] According to some evidence-based experts' opinions from the FMEA team members, each FM will obtain risk priority number (RPN) to represent the level of its risk. Subsequently, some recommendations are provided on how FMs might be controlled to prevent them from having negative effects on the system.…”

Risk assessment on multimodal transport network based on quality function deployment

“…On the basis of historical accident data and expert opinions, Zhang et al 28 established a Bayesian belief network (BBN) risk assessment model that could be used to assess the consequences of accidents, which helped relevant companies to fully understand the probability of marine risks and the consequences of the accidents. Besides, based on subjective probability risk assessment technology, Yazdi 29 established a heuristic optimization model of linear mathematical programming based on triangular intuitionistic fuzzy numbers, which made up for the various deficiencies of multi‐objective decision‐making methods. Haghighi et al 30 used regression modeling technology to estimate the risk incidence of single or combined lines in the network, and then determined the key paths and links in the transport network.…”

Ignorance‐aware safety and reliability analysis: A heuristic approach

“…Obviously, decision‐making science is an integrated part of safety and reliability analysis, which is inherently based on uncertainty . Uncertainty conceptually consists of two forms as structural and parameter uncertainties, respectively, reflecting the ambiguity in a model selection and the uncertainties in the input data . Once a reliable and appropriate dataset is available, both structural and parameter uncertainties can be properly accounted for in the safety and reliability analysis of the engineering system.…”

“…5,6 Uncertainty conceptually consists of two forms as structural and parameter uncertainties, respectively, reflecting the ambiguity in a model selection and the uncertainties in the input data. [7][8][9] Once a reliable and appropriate dataset is available, both structural and parameter uncertainties can be properly accounted for in the safety and reliability analysis of the engineering system. However, in the absence of appropriate data or improper availability of data, 10 the decision-making techniques have been widely developed and further integrated to deal with this situation.…”