Modified FMEA hazard identification for cross-country petroleum pipeline using Fuzzy Rule Base and approximate reasoning

Hassan, Shamsu; Wang, Jin; Kontovas, Christos A.; Bashir, Musa

doi:10.1016/j.jlp.2021.104616

Cited by 44 publications

(21 citation statements)

References 41 publications

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“…This section describes the methodology for the development of the proposed ER model that is applied to the risk of 'loss of containment due to third-party intervention' in a pipeline. The methodology takes into account and builds upon the previous work of the authors (Hassan, 2018;Hassan et al, 2019;Hassan et al, 2022b;Hassan et al, 2022a). Figure 1 provides the methodology schematic for the proposed model.…”

Section: Evidential Reasoning Application/methodologymentioning

confidence: 99%

A Decision-Support System for Assessing Cross-Country Pipeline Systems: Approach Based on Evidential Reasoning and Cost–Benefit Analysis

Hassan¹,

Kontovas

Bashir

et al. 2023

J. Pipeline Syst. Eng. Pract.

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This paper outlines a decision support model based on Evidential Reasoning (ER) and Cost-Benefit-Analysis (CBA) to support the assessment of risk control measures for pipeline loss of containment following third party damage. The model identifies the main Risk Control Options (RCOs) as basic attributes, which are grouped into three categories: technical or technological, governmental and managerial solutions. The CBA examines the costs associated with the loss of containment in regard to human safety, the economic and environmental aspects that are required to reduce or eliminate the threat of using RCOs. ER is chosen because of its capability in dealing with Multiple Criteria Decision Making (MCDM) problems with uncertainties, aggregation of conflicting information and the hybrid nature of the information. This corresponds with the challenges of analysing different and often conflicting information identified in this study. The results show the attributes of each decision, their effectiveness in reducing the failure likelihood and the estimated cost of each attribute. The study reduces the complexity of the decision-making into a simple hierarchical output and provide guidance to the infrastructure operator. This makes it possible for the operator to select one or more risk reduction attributes and immediately see a potential reduction in the failure likelihood. Furthermore, the study provides knowledge to the operator on budgetary expenditure required to implement the RCO.

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Section: Evidential Reasoning Application/methodologymentioning

confidence: 99%

A Decision-Support System for Assessing Cross-Country Pipeline Systems: Approach Based on Evidential Reasoning and Cost–Benefit Analysis

Hassan¹,

Kontovas

Bashir

et al. 2023

J. Pipeline Syst. Eng. Pract.

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“…The results demonstrated that the proposed approach was very effective in prioritizing risks by taking uncertainty into account. In addition, to handle the uncertainties of classical-FMEA in other literature, the hybrid perception of fuzzy rule-based theories has been given a lot of attention [42][43][44].…”

Section: Literature Reviewmentioning

confidence: 99%

Sustainable Food Production: An Intelligent Fault Diagnosis Framework for Analyzing the Risk of Critical Processes

et al. 2022

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Fault diagnosis and prognosis methods are the most useful tools for risk and reliability analysis in food processing systems. Proactive diagnosis techniques such as failure mode and effect analysis (FMEA) are important for detecting all probable failures and facilitating the risk analysis process. However, significant uncertainties exist in the classical-FMEA when it comes to ranking the risk priority numbers (RPNs) of failure modes. Such uncertainties may have an impact on the food sector’s operational safety and maintenance decisions. To address these issues, this research provides a unique FMEA framework for risk analysis within an edible oil purification facility that is based on certain well-known intelligent models. Fuzzy inference systems (FIS), adaptive neuro-fuzzy inference systems (ANFIS), and support vector machine (SVM) models are among those used. The findings of the comparison of the proposed FMEA framework with the classical model revealed that intelligent strategies were more effective in ranking the RPNs of failure modes. Based on the performance criteria, it was discovered that the SVM algorithm classifies the failure modes more accurately and with fewer errors., e.g., RMSE = 7.30 and MAPE = 13.19 with that of other intelligent techniques. Hence, a sensitivity FMEA analysis based on the SVM algorithm was performed to put forward suitable maintenance actions to upgrade the reliability and safety within food processing lines.

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“…Owing to the uncertain and complex nature of risk, one cannot accurately estimate the risk priority evaluation in real life. Plenty of uncertain tools have been set to overcome the deficiencies of RPN in classical FMEA [14,15]. The fuzzy technique is one of the most active and the broadest fields of application research in improving FMEA [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Risk Priority Evaluation of Power Transformer Parts Based on Hybrid Fmea Framework Under Hesitant Fuzzy Environment

Zeng

Chen

et al. 2022

FU Mech Eng

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The power transformer is one of the most critical facilities in the power system, and its running status directly impacts the power system's security. It is essential to research the risk priority evaluation of the power transformer parts. Failure mode and effects analysis (FMEA) is a methodology for analyzing the potential failure modes (FMs) within a system in various industrial devices. This study puts forward a hybrid FMEA framework integrating novel hesitant fuzzy aggregation tools and CRITIC (Criteria Importance Through Inter-criteria Correlation) method. In this framework, the hesitant fuzzy sets (HFSs) are used to depict the uncertainty in risk evaluation. Then, an improved HFWA (hesitant fuzzy weighted averaging) operator is adopted to fuse risk evaluation for FMEA experts. This aggregation manner can consider different lengths of HFSs and the support degrees among the FMEA experts. Next, the novel HFWGA (hesitant fuzzy weighted geometric averaging) operator with CRITIC weights is developed to determine the risk priority of each FM. This method can satisfy the multiplicative characteristic of the RPN (risk priority number) method of the conventional FMEA model and reflect the correlations between risk indicators. Finally, a real example of the risk priority evaluation of power transformer parts is given to show the applicability and feasibility of the proposed hybrid FMEA framework. Comparison and sensitivity studies are also offered to verify the effectiveness of the improved risk assessment approach.

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Modified FMEA hazard identification for cross-country petroleum pipeline using Fuzzy Rule Base and approximate reasoning

Cited by 44 publications

References 41 publications

A Decision-Support System for Assessing Cross-Country Pipeline Systems: Approach Based on Evidential Reasoning and Cost–Benefit Analysis

A Decision-Support System for Assessing Cross-Country Pipeline Systems: Approach Based on Evidential Reasoning and Cost–Benefit Analysis

Sustainable Food Production: An Intelligent Fault Diagnosis Framework for Analyzing the Risk of Critical Processes

Risk Priority Evaluation of Power Transformer Parts Based on Hybrid Fmea Framework Under Hesitant Fuzzy Environment

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