Applications of fuzzy faulty tree analysis and expert elicitation for evaluation of risks in LPG refuelling station (original) (raw)
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Journal of Loss Prevention in the Process Industries
A method is presented for analysis of reliability of complex engineering systems using information from fault tree analysis and uncertainty/imprecision of data. Fuzzy logic is a mathematical tool to model inaccuracy and uncertainty of the real world and human thinking. The method can address subjective, qualitative, and quantitative uncertainties involving risk analysis. Risk analysis with all the inherent uncertainties is a prime candidate for Fuzzy Logic application. Fuzzy logic combined with expert elicitation is employed in order to deal with vagueness of the data, to effectively generate basic event failure probabilities without reliance on quantitative historical failure data through qualitative data processing.The proposed model is able to quantify the fault tree of LPG refuelling facility in the absence or existence of data. This paper also illustrates the use of importance measures in sensitivity analysis. The result demonstrates that the approach is an apposite for the pro...
Journal of Failure Analysis and Prevention, 2018
Chemical process plants, especially the oil and gas plants operating under severe processing conditions and dealing with hazardous materials, are susceptible to catastrophic accidents. Thus safety risk assessment is vital in designing effective strategies for preventing and mitigating potential accidents. Fault tree analysis (FTA) is a well-known technique to analyze the risks related to a specific system. In the conventional FTA, the ambiguities and uncertainties of basic events (BEs) cannot be handled effectively. Therefore, employing fuzzy set theory helps probabilistic estimation of BEs and subsequently the top event (TE). This study presents an integrated approach to fuzzy set theory and FTA for handling uncertainty in the risk analysis of chemical process plants. In this context, the worst case scenario based on a qualitative risk analysis is selected first and then the fuzzy FTA is established. Finally, different fuzzy aggregation and defuzzification approaches are employed to obtain the probability of each BE and TE, the output of each approach is compared to the occurrence probability of TE, and the critical BEs are ranked. The proposed methodology is applied to the fuzzy probabilistic analysis of hydrocarbon release in the BP tragic accident of March 2005. The results indicate that the proposed approach is very effective in risk analysis considering uncertainty reduction or handling.
Fuzzy based risk prioritisation in an auto LPG dispensing station
Safety Science, 2018
A fuzzy rule based inference model assessing the failure modes for risk ranking in FMEA to manage risks and make maintenance decisions is applied to a LPG refuelling station in this paper. Normally in FMEA, risk priority number (RPN) is determined by multiplication of feature scores that are inferred from the degree or probability of occurrence, severity and non detection of the problem, without taking into consideration the relative importance of factors. In fuzzy approach, linguistic assessment of factors is evaluated to obtain risk priority number. A rule based fuzzy inference engine generates the priority ranks of identified failure modes. Direct evaluation is possible with the aid of grey theory by assigning weights to the features in the absence of expertise to develop an inference rule base. The GRA approaches can solve the problem of risk prioritising, which needs expertise to develop rule base while building the fuzzy inference system. By applying fuzzy FMEA and fuzzy logic with grey relational approach (GRA), expert linguistic opinions are used to rank the identified failure modes and the results are presented. The risk of the failure modes are ranked in an inclusive approach based on the fuzzy domain projections. It is effective and feasible to handle various types of uncertainties, such as incompleteness, fuzziness, imprecision, and so on, in the risk analysis process.
Journal of Loss Prevention in the Process Industries, 2017
Human error is an important consideration in process industries. In this study a framework is developed based on the application of Fuzzy HEART and Expert elicitation for performing quantification of human error probability with an application to refuelling operation in an LPG refueling station. HEART technique analyses the tasks and quantifies likelihood of unreliability of activities which disturb successful completion of the process. Implication of fuzzy pursues uncertainties by defining linguistic variables for an expert's opinion. An expert weighing approach along with a structured expert-elicitation approach is employed to increase the fidelity of the technique. The results obtained are properly compared with the results generated through CREAM, a second generation technique. Results have helped to identify curative measures which are both cost effective and easy to implement with respect to human activities to improve safety at LPG dispensing station. This approach enables HEART technique to accommodate opinions of expert with ambiguity and uncertainty and to yield appreciable results like a second generation technique. Application of this technique can be expanded to process industries and establishments where operator actions take place.
AIP Conference Proceedings, 2019
Probabilistic safety assessment (PSA) has been extensively implemented to assess the performance of nuclear power plant (NPP) safety systems. One well-known modeling approach in NPP PSA is a fault tree analysis (FTA). A fault tree is a graphical representation of possible failure scenarios of the system being evaluated. To estimate the top event failure probability, a quantitative analysis needs to be performed based on those scenarios. Prior to performing quantitative analysis, basic events' failure probabilities of the system fault tree need to be provided well in advance. Conventional FTA assumes that basic events always have precise probability distributions characterizing their lifetime to failure. However, in practical applications, this is not the case. For example, a new system will not have sufficient operating experiences to probabilistically estimate reliabilities of their components. To deal with this limitation, a number of approaches has been developed and proposed. Each approach offers advantageous but also has disadvantageous. Since the results of FTA will be used to verify NPP designs, it is necessary to select the most suitable approach. It is, therefore, essential to clearly understand the strengths and weaknesses of each approach. The purpose of this study is to review the implementation of various FTA approaches in NPP PSA. The strengths and weaknesses of each approach are also discussed. To achieve research objectives, this study classified those FTA approaches into conventional FTA and fuzzy FTA. Fuzzy FTA is further grouped into fuzzy hybrid FTA and fuzzy based FTA. This study concludes that safety analysts need to, firstly, confirm the type of reliability data at hands. Secondly, if epistemic uncertainty is essential and need to be considered in the study being performed, fuzzy based FTA should be applied. Otherwise, safety analysts should apply conventional FTA or fuzzy hybrid FTA depending on how the basic events' failure probabilities are generated.
Risk Assesing by Fuzzy Logic-Algorithmic Fault Tree
The new method for risk assessment of the man-machine system functioning is proposed. The feature of the method is combining the fuzzy fault tree analysis and theory of fuzzy reliability of algorithmic processes. This provides a consideration from a unified framework the structural accidents with algorithms of its detecting and avoiding. Method also allows to use fuzzy " if-then " rules about man-machine and environment factors influencing upon probabilities of various events connected with each hazard situation.
A CASE STUDY ON FUZZY LOGIC-BASED RISK ASSESSMENT IN OIL AND GAS INDUSTRY
Risk assessment is a process of categorizing and measurement of risk related outcomes from a specific incident and in a particular scenario. While risk itself is considered as the combination of likelihood and severity of the consequences of hazards. Typically, the qualitative approach of risk based inspection (RBI) is applied in oil and gas industries to measure the risk levels of hazards. But with this qualitative approach sometime the risk ranking ties among the different factors can lead to problem in selecting the most critical factor. To address the problem, this study aims to develop a fuzzy logic-base risk assessment model using a quantitative approach of RBI that will assist to mitigate the risk ties in risk ranking process of hazard. In this proposed model, fuzzy membership functions and ranges have been assigned for likelihood, severity of consequences and for total risk levels. A case study on ammonia hazard is presented to demonstrate the vitality of the proposed fuzzy risk assessment model with samples of four categories (people, environment, asset and reputation) from an oil and gas industry. The outcomes of this study indicate that the developed model has a strong potential application in oil and gas industry in assessing the severity levels of risk, and resolving risk ranking ties.
Fuzzy evidence theory and Bayesian networks for process systems risk analysis
Human and Ecological Risk Assessment: An International Journal, 2018
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.
A Question on Using Fuzzy Set Theory and Its Extensions in Safety and Reliability
COMPUTATIONAL RESEARCH PROGRESS IN APPLIED SCIENCE & ENGINEERING (CRPASE), 2020
In order to analyze the safety and reliability of a system, different types of probabilistic structural methods such fault tree analysis (FTA), event tree analysis (ETA), Bayesian Network (BN) can be used. In such probabilistic methods, the failure probability of an event as root events must be obtained. Reliability Data handbook database can be used to obtain the failure probability of the events. However, in some case, there is no data about the failure probability of event. In such a case, expert judgment using fuzzy set theory and its extensions are utilized as an alternative in order to deal with the subjective uncertainty. However, the common existing expert judgment approaches still suffer from the logical lack of reliability in the experts' opinions elicitation procedure. This paper analyzes the use of fuzzy set theory and its extensions in safety and reliability analysis as well as discussing how they suffer from lack of reliability As a result, it should make researchers in that situation deeply think about finding out of a better alternative using experts' judgment-based methods.
A fuzzy Bayesian network approach for risk analysis in process industries
Process Safety and Environmental Protection, 2017
Fault tree analysis is a widely used method of risk assessment in process industries. However, the classical fault tree approach has its own limitations such as the inability to deal with uncertain failure data and to consider statistical dependence among the failure events. In this paper, we propose a comprehensive framework for the risk assessment in process industries under the conditions of uncertainty and statistical dependency of events. The proposed approach makes the use of expert knowledge and fuzzy set theory for handling the uncertainty in the failure data and employs the Bayesian Network modeling for capturing dependency among the events and for a robust probabilistic reasoning in the conditions of uncertainty. The effectiveness of the approach was demonstrated by performing risk assessment in an ethylene transportation line unit in an ethylene oxide (EO) production plant.