Failure Mode And Effects Analysis Approach Research Articles

Risk assessment of crane operation hazards using modified FMEA approach with Z-number and set pair analysis

In view of two key deficiencies of failure mode and effects analysis (FMEA) in crane operation hazard risk assessment—information loss and hazard grade lack, a crane operation hazard risk assessment model based on the Z-number and set pair analysis (Z-SPA) is proposed. In this research, the Z-number is used to address the information uncertainty. To reduce the risk losing evaluation information in the conversion of the Z-number into a crisp number, it is translated into an interval number. In this assessment model, the grades of crane operation hazards are determined using grade discrimination rules and a score function of a connection number based on set pair analysis. Two case studies are presented to exemplify the effectiveness of the approach. The first case validates the proposed method, and the second case illustrates the application of the modified FMEA to crane operation hazard risk analysis. Furthermore, the assessment results are analyzed and compared with previous studies to highlight the superiority of the proposed model.

A Decision Support Model for Mitigating Supply Chain Risks Based on a Modified FMEA, Multi-Objective Optimization and Multi-Criteria Decision-Making Approach

Abstract Recent years have seen a huge development in the subject of supply chain risk management. In this increasingly uncertain world, the use of practical and effective tools for decision making and risk mitigation has become more necessary than ever. In this research, mitigation strategies for a tier one multinational company operating in the automotive industry and providing an assembly operation to final customer Renault Tanger and Renault SOMACA were prioritized according to their effectiveness, as well as their implementation costs. Based on research in the literature and the opinions of experts in the field. 44 risks and 55 mitigation strategies were identified. FMEA (Failure Modes and Effects Analysis) method was used based on the latest AIAG 2019 edition to filter and identify the risks to be prioritized, we used then a multi-objective optimization approach to identify the mitigation strategies that constitute the Pareto front for each of the risks and finally used the EDAS method for the final ranking of the strategies. Our case revealed that strategies like ensuring elaborating a contingency planning and defining the responsibilities, imposing contractual obligations on subcontractors, applying a flexible supply contract were found to be relevant risk mitigation strategies for the company. Managers interested in mitigating risk can deploy this model to prioritize risk mitigation strategies.

Evaluation of the effectiveness of defense measures during radiological risk assessment in gynecological brachytherapy

The implementation of risk analysis to all those practices that work with ionizing radiation is of paramount importance. Thanks to the studies published on risk assessment in radiation medicine, a culture on this subject is being created, which has led to the systematization of different methods created for this purpose. Such is the case of the Failure Mode and Effects Analysis (FMEA), the basis on which this research has been developed. The American Association of Physicist in Medicine (AAMP) TG-100 report, which provides all the details on the use of this technique, is used as main reference. On the other hand, the recommendations recently published by AAMP TG-275 report are also used, applying the FMEA methodology to the high dose rate gynecological brachytherapy (B-HDR-GYN), among other practices. The novelty of this research is the implementation of an operational algorithm that improves the analytical capacity of the traditional FMEA approach by allowing the measurement of the effectiveness of defense measures within it. In this study, when the defense measures interact in the FMEA, a decrease of four RPN values of the failure modes (FM) occurs; compared to reference values. This algorithm is based on a synergy of the risk matrix (RM) approach and the information on FMEA available in the TG-100 and TG-275.

A new approach to risk assessment in failure mode and effect analysis based on engineering textual data

Failure Mode and Effect Analysis (FMEA) is a valuable tool for improving the quality of products and service systems. However, traditional FMEA methods require examining various engineering textual materials and attending multiple meetings, which can be time-intensive. Additionally, accurately evaluating the severity (S), occurrence (O), and detection (D) of failure modes is essential to ensure the accuracy of FMEA results. Despite efforts by researchers to improve the efficiency of FMEA, the evaluation of these risk factors (S, O, and D) still relies too heavily on a manual and inefficient process. To address these issues, this paper proposes a machine learning-enabled FMEA approach. This new approach combines the strengths of the BERT (Bidirectional Encoder Representations from Transformers) model in transforming textual failure descriptions into word vectors, the advantages of the VSM (Vector Space Model) in determining semantic similarity between different failure modes, and the merits of TOPSIS based on objective weights in handling multicriteria risk assessment. The proposed approach was applied to an actual case study for failure mode and effect analysis in auto parts processing. Comparisons between the proposed method and conventional FMEA were conducted to demonstrate the effectiveness of the new approach.

Enhancing System Safety and Reliability through Integrated FMEA and Game Theory: A Multi-Factor Approach

This study aims to address the limitations of traditional Failure Mode and Effect Analysis (FMEA) in managing safety and reliability within complex systems characterized by interdependent critical factors. We propose an integrated framework that combines FMEA with the strategic decision-making principles of Game Theory, thereby enhancing the assessment and mitigation of risks in intricate environments. The novel inclusion of the Best Worst Method (BWM) and Pythagorean fuzzy uncertain linguistic variables refines the accuracy of risk evaluation by overcoming the inherent deficiencies of conventional FMEA approaches. Through sensitivity analysis, the framework’s efficacy in identifying and prioritizing failure modes is empirically validated, guiding the development of targeted interventions. The practical application of our methodology is demonstrated in a comprehensive healthcare system analysis, showcasing its versatility and significant potential to improve operational safety and reliability across various sectors. This research is particularly beneficial for systems engineers, risk managers, and decision-makers seeking to fortify complex systems against failures and their effects.

Proposal of a facilitating methodology for fuzzy FMEA implementation with application in process risk analysis in the aeronautical sector

PurposeThis study aims to propose a facilitating methodology for the application of Fuzzy FMEA (Failure Mode and Effect Analysis), comparing the traditional approach with fuzzy variations, supported by a case application in the aeronautical sector.Design/methodology/approachBased on experts' opinions in risk analysis within the aeronautical sector, rules governing the relationship between severity, occurrence, detection and risk factor were defined. This served as input for developing a fuzzyfied FMEA tool using the Matlab Fuzzy Logic Toolbox. The tool was applied to the sealing process in a company within the aeronautical sector, using triangular and trapezoidal membership functions, and the results were compared with the traditional FMEA approach.FindingsThe results of the comparative application of traditional FMEA and fuzzyfied FMEA using triangular and trapezoidal functions have yielded valuable insights into risk analysis. The findings indicated that fuzzyfied FMEA maintained coherence with the traditional analysis in identifying higher-risk effects, aligning with the prioritization of critical failure modes. Additionally, fuzzyfied FMEA allowed for a more refined prioritization by accounting for variations in each variable through fuzzy rules, thereby improving the accuracy of risk analysis and providing a more realistic representation of potential hazards. The application of the developed fuzzyfied FMEA approach showed promise in enhancing risk assessment in the aeronautical sector by considering uncertainties and offering a more detailed and context-specific analysis compared to conventional FMEA.Practical implicationsThis study emphasizes the potential of fuzzyfied FMEA in enhancing risk assessment by accurately identifying critical failure modes and providing a more realistic representation of potential hazards. The application case reveals that the proposed tool can be integrated with expert knowledge to improve decision-making processes and risk mitigation strategies within the aeronautical industry. Due to its straightforward approach, this facilitating methodology could also prove beneficial in other industrial sectors.Originality/valueThis paper presents the development and application of a facilitating methodology for implementing Fuzzy FMEA, comparing it with the traditional approach and incorporating variations using triangular and trapezoidal functions. This proposed methodology uses the Toolbox Fuzzy Logic of Matlab to create a fuzzyfied FMEA tool, enabling a more nuanced and context-specific risk analysis by considering uncertainties.

A new approach to failure mode and effect analysis under linguistic Z-number: A case study of CNC tool holders

Failure Mode and Effects Analysis (FMEA), as the most important component of risk management, has been widely used in the engineering field. Although the method is widely used, the traditional FMEA approach does not take into account the issue of decision maker's mental preference, which seriously affects the accuracy of risk ranking results. To improve these shortcomings, we fuse the language set and Z-number to propose the language Z-number to express the decision information. Meanwhile, we propose a new linguistic Z-number weight Muirhead mean (LZNWMM) operator and use it to aggregate the information. Finally, we propose a new FMEA framework, which uses the proposed operator to aggregate information and calculate attribute weights using the fuzzy best-worst method. Using this method applied to a computer numerical control (CNC) tool holder manufacturer, its usability and validity are verified by comparing it with the traditional FMEA method, LZNWA method and LZNBM method.

A Minimum Cost Consensus-Based Failure Mode and Effect Analysis Framework Considering Experts' Limited Compromise and Tolerance Behaviors.

This study proposes a minimum cost consensus-based failure mode and effect analysis (MCC-FMEA) framework considering experts' limited compromise and tolerance behaviors, where the first behavior indicates that a failure mode and effect analysis (FMEA) expert might not tolerate modifying his/her risk assessment without limitations, and the second behavior indicates that an FMEA expert will accept risk assessment suggestions without being paid for any cost if the suggested risk assessments fall within his/her tolerance threshold. First, an MCC-FMEA with limited compromise behaviors is presented. Second, experts' tolerance behaviors are added to the MCC-FMEA with limited compromise behaviors. Theoretical results indicate that in some cases, this MCC-FMEA with limited compromise and tolerance behaviors has no solution. Thus, a minimum compromise adjustment consensus model and a maximum consensus model with limited compromise behaviors are developed and analyzed, and an interactive MCC-FMEA framework, resulting in an FMEA problem consensual collective solution, is designed. A case study, regarding the assessment of COVID-19-related risk in radiation oncology, and a detailed sensitivity and comparative analysis with the existing FMEA approaches are provided to verify the effectiveness of the proposed approach to FMEA consensus-reaching.

A hybrid integrated multi-criteria decision-making approach for risk assessment: a study of automotive parts industry

PurposeThis paper aims to enable the analysts of reliability and safety systems to evaluate the risk and prioritize failure modes ideally to prefer measures for reducing the risk of undesired events.Design/methodology/approachTo address the constraints considered in the conventional failure mode and effects analysis (FMEA) method for criticality assessment, the authors propose a new hybrid model combining different multi-criteria decision-making (MCDM) methods. The analytical hierarchy process (AHP) is used to construct a criticality matrix and calculate the weights of different criteria based on five criticalities: personnel, equipment, time, cost and quality. In addition, a preference ranking organization method for enrichment evaluation (PROMETHEE) method is used to improve the prioritization of the failure modes. A comparative work in which the robust data envelopment analysis (RDEA)-FMEA approach was used to evaluate the validity and effectiveness of the suggested approach and simplify the comparative analysis.FindingsThis work aims to highlight the real case study of the automotive parts industry. Using this analysis enables assessing the risk efficiently and gives an alternative ranking to that acquired by the traditional FMEA method. The obtained findings offer that combining of two multi-criteria decision approaches and integrating their outcomes allow for instilling confidence in decision-makers concerning the risk assessment and the ranking of the different failure modes.Originality/valueThis research gives encouraging outcomes concerning the risk assessment and failure modes ranking in order to reduce the frequency of occurrence and gravity of the undesired events by handling different forms of uncertainty and divergent judgments of experts.

Impact of Financing Problems on Baitul Mal wa Tamwil during Covid-19

Abstract: This study evaluates the cause and effect of financing problems on Baitul Mal wa Tamwil (BMT) during COVID-19 and analyzes the strategy taken by BMT to handle financing problems. This study is qualitative research. The primary and secondary data were obtained from observations, interviews, and documentation. The informants of the study are top management and members of BMT Al Hidayah Kotaraja Lombok Timur. The data are analyzed using the FMEA (Failure Mode and Effect Analysis) approach. The FMEA method is a systematic method used to identify and prevent problems in a company's product or business process. All process is calculated in a score, namely Risk Priority Number (RPN), and shown in the Pareto diagram. Financing problems are categorized as substandard, doubtful, and bad financing. The causes of financing problems are, among others, the employee’s lack of ability to assess the personality of the prospective debtors in fulfilling their installments, and natural disasters (the COVID-19 pandemic) that may affect someone’s financial ability. Financing problems have an impact on the emergence of financing risk. BMT Al Hidayah Kotaraja is also directly impacted by the inefficient money flow because monthly profit sharing still needs to be distributed to the members. The main factor causing this problem is staff members' inability to evaluate potential debtors. Additionally, the company's approach to these financing problems has not been working. The management, in that case, focused on the debtors instead of their employee’s capability. In that circumstance, the management ought to focus on improving the capacity of their staff to evaluate potential members. According to this study's findings, the FMEA is applicable in resolving finance issues in BMT. Originality/Value: The research related to risk management in BMT, especially NPF caused by the COVID-19 pandemic, using the FMEA (Failure Mode and Effect Analysis) method. FMEA is a systematic method used to identify and prevent problems with a company's products or business processes from occurring. FMEA is used to examine risk management in banking and provides convenience to the management in making decisions for planning, maintenance, and development purposes. The FMEA method in this study identifies and prevents problems in BMT.

Impact of Financing Problems on Baitul Mal wa Tamwil during Covid-19

Abstract: This study evaluates the cause and effect of financing problems on Baitul Mal wa Tamwil (BMT) during COVID-19 and analyzes the strategy taken by BMT to handle financing problems. This study is qualitative research. The primary and secondary data were obtained from observations, interviews, and documentation. The informants of the study are top management and members of BMT Al Hidayah Kotaraja Lombok Timur. The data are analyzed using the FMEA (Failure Mode and Effect Analysis) approach. The FMEA method is a systematic method used to identify and prevent problems in a company's product or business process. All process is calculated in a score, namely Risk Priority Number (RPN), and shown in the Pareto diagram. Financing problems are categorized as substandard, doubtful, and bad financing. The causes of financing problems are, among others, the employee’s lack of ability to assess the personality of the prospective debtors in fulfilling their installments, and natural disasters (the COVID-19 pandemic) that may affect someone’s financial ability. Financing problems have an impact on the emergence of financing risk. BMT Al Hidayah Kotaraja is also directly impacted by the inefficient money flow because monthly profit sharing still needs to be distributed to the members. The main factor causing this problem is staff members' inability to evaluate potential debtors. Additionally, the company's approach to these financing problems has not been working. The management, in that case, focused on the debtors instead of their employee’s capability. In that circumstance, the management ought to focus on improving the capacity of their staff to evaluate potential members. According to this study's findings, the FMEA is applicable in resolving finance issues in BMT. Originality/Value: The research related to risk management in BMT, especially NPF caused by the COVID-19 pandemic, using the FMEA (Failure Mode and Effect Analysis) method. FMEA is a systematic method used to identify and prevent problems with a company's products or business processes from occurring. FMEA is used to examine risk management in banking and provides convenience to the management in making decisions for planning, maintenance, and development purposes. The FMEA method in this study identifies and prevents problems in BMT.

Risk Analysis in the Food Cold Chain Using Decomposed Fuzzy Set-Based FMEA Approach

Risk analysis is employed across various domains, including the increasingly vital food supply chain, particularly highlighted by the COVID-19 pandemic. This study focuses on applying decomposed fuzzy sets (DFS), a novel extension of intuitionistic fuzzy sets, within the context of the food cold chain. The objective is to develop “Decomposed Fuzzy Set-Based FMEA (DF FMEA)” by extending the well-known failure mode and effect analysis (FMEA) method to DFS, to assess risks in the food cold chain. The functional and dysfunctional questions related to the severity, occurrence, and detectability of the identified risks; they were addressed to three experts working on the food cold chain. The purpose is to prevent an inconsistent assignment considering the uncertainty and indecision of decision makers. Due to the implementation of the DF FMEA, the identified risks were prioritized as follows: “Financial Risks” held the highest priority, followed by “Delivery Risks”, “Technological Ability Risks”, “Environmental Risks”, “Quality Risks”, and “Social Risks” with the lowest priority. The study’s practical impact lies in the innovative risk assessment method. By considering decision makers’ preferences and uncertainties, the DF FMEA approach enhances informed decision making. This contributes to a robust framework for addressing risks in the food cold chain, aiding practitioners in more effective risk management.

A New Extended Approach to Reduce Admission Time in Hospital Operating Rooms Based on the FMEA Method in an Uncertain Environment

Operating rooms (ORs) are one of the essential hospital resources and optimal management can result in efficient OR usage. The admission time reduction before surgery in the ORs can lead to on-time surgery and efficient use of ORs. This study addresses this issue by identifying the main failure modes that cause delays in ORs. The conventional methodology known as Failure Mode and Effects Analysis (FMEA) represents one of the prevailing techniques utilized for the purpose of ascertaining failure modes within a given process. This involves the assignment of numerical scores to each failure mode, with the intention of utilizing the resultant Risk Priority Number (RPN) to facilitate the identification of said failure modes. However, RPN scoring has been criticized for some deficiencies. This study proposes a three-phase approach to address some of the shortcomings of the FMEA method. The initial stage involves utilizing the FMEA approach to recognize failure modes and assess the crucial elements of RPN. Following this, the second stage employs the Z-BWM technique and expert insights to determine the weights of the five essential factors. Lastly, in the third phase, risks are prioritized using the proposed Z-ARAS method based on the outputs of the previous phases. This approach considers the uncertainty in the determining factors and assigns different weights to them, while also taking into account the reliability of the risks through the Z-Number theory. Finally, comparing the proposed approach with other traditional approaches, reinforces the usefulness of the proposed method in evaluating failure modes in OR management.

A novel FMEA approach based on probabilistic linguistic best-worst method and TOPSIS with application to marine diesel fuel injection system

The failure mode and effect analysis (FMEA) is an effective tool to analyze risks and potential effects of complex systems, and it is one of the most widely used risk analysis methods for complex systems as there often exists various factors that could affect the operation of the complex systems. Conventional FMEA methods have been limited to using crisp values to represent the assessments, which has been criticized for having many deficiencies. Marine diesel fuel injection system is an important part of marine diesel engine, and its failure could directly affect the performance of the marine diesel engine and even impact the safe operation of the ship. However, little attention has been paid to the FMEA of the marine diesel fuel injection system. To this end, this paper presents a novel FMEA method based on the best-worst method (BWM) and TOPSIS method with probabilistic linguistic term set (PLTS). Firstly, the PLTS is used to represent the uncertain and linguistic judgments of experts. Then, the BWM is extended with PLTS to determine the weights of different elements for FMEA, and the TOPSIS is extended with PLTS to assess and rank different failure modes. Finally, a case study on marine diesel fuel injection is presented, and the most critical failures are identified for improvement measures. The results show that the proposed method could help managers and engineerings identify the most important failure modes for marine diesel fuel injection system.

Challenges experienced during pharmacy automation and robotics implementation in JCI accredited hospital in the Arabian Gulf area: FMEA analysis-qualitative approach

BackgroundPharmacy automation and robotics implementation are essential aspects of healthcare facilities. It streamlines the medication dispensing process and significantly reduces medication errors. However, implementing automation and robotics in pharmacies comes with its challenges. We aim to detect and rectify potential dangers in the pharmacy workflow by utilizing the Failure Mode and Effects Analysis (FMEA) methodology; this is expected to augment performance and increase profitability. Materials and methodsIn this study, we conducted an FMEA analysis using a qualitative approach to identify the challenges experienced during pharmacy automation and robotics implementation in a Joint Commission International(JCI) accredited hospital in the Arabian Gulf area. The pharmacy processes and procedures were mapped in a Flow chart to visualize the pharmacy workflow, including highlighting the risks that were found. Then these risks were arranged as Potential failure modes and added to the table as 9 main points for each RPNs were calculated, and then the 9 points were prioritized for the action plans. ResultsVia applying traditional Risk Priority Number (RPN) FMEA, the Pharmacy board identified the process stages marked risky failure modes through several FMEAs, calculating the total RPNs at the implementation phase. It revealed several challenges, including staff training, technical issues, and inadequate communication. Furthermore, the study resulted in corrective and intervention steps. ConclusionPharmacy automation and robotics implementation is a complex process that requires proper planning, preparation, and execution. The FMEA approach effectively identifies potential problems and evaluates their impact on the pharmacy system. Nine major failure modes appeared to be risky stages with high RPN scores. Therefore, multiple interventions were done during the implementation to enhance the knowledge of challenges faced during the implementation of the automation process and solve it. Future studies should address the identified challenges and develop strategies to mitigate them.

A fully data-driven FMEA framework for risk assessment on manufacturing processes using a hybrid approach

The Failure Mode and Effect Analysis (FMEA) is a widely used method that effectively identifies and prioritizes potential risks in a given system or process. However, traditional and modified versions of FMEA are often criticized for their subjective assessments, inadequate risk prioritization methods, and lack of consideration of the importance level of risk factors. To address these issues, this study introduces a data-driven FMEA approach. Specifically, the proposed approach utilizes data-driven risk factors to determine objective rankings of failure modes. This study uses the frequency and stability of failures, time and product loss cost due to failure as objective and data-driven risk factors. These factors enable a more precise description of the influence of risk factors on failure modes. The Modified Criteria Ranking Importance with Intra-criteria Correlation (M-CRITIC) method is employed to assign weights to the identified risk factors, which indicates their level of importance in analysis. Additionally, the recently proposed Alternative by Alternative Comparison (ABAC) method is used to derive the risk priorities of failure modes. The effectiveness and applicability of the developed approach are demonstrated through a case study focused on manufacturing process risk analysis in the food industry. Furthermore, this study contributes to the growing trend toward objective risk calculations for FMEA and highlights the importance of using data-driven models for risk analysis.

Risk evaluation of photovoltaic power systems: An improved failure mode and effect analysis under uncertainty

Photovoltaic (PV) power systems are confronted with many failure risks threatening operational security and leading to adverse impacts on the sustainable development. This paper identified the potential PV system failures and extended the traditional failure mode and effects analysis (FMEA) that has been questioned due to its inadequacy in industrial applications. First, the risk factor evaluation system was expanded from the original 3 indicators to 7 indicators associated with PV system failures, including personal safety, power loss, property damage, frequency of failure, visualization, maintenance expense, and technical difficulty. Second, a modified FMEA approach combining rough set theory, cloud model theory and multiplicative Multi-objective Optimization by Ratio Analysis (MULTIMOORA) method was developed. The proposed approach could not only settle various uncertainties (i.e., intrapersonal uncertainty, interpersonal uncertainty, and randomness) by integrating cloud model and rough sets, but also introduce the triple subordinate method MULTIMOORA to obtain a final risk ranking of PV system failures. Finally, the improved FMEA model was adopted to a PV system failure risk assessment case, verifying its effectiveness and superiority. It revealed that hot spots and disconnection of underground cables were the riskiest potential failures in PV systems. Essential risk mitigation measures should be implemented in advance to maintain the safety and promote the sustainable development of PV systems.

A developed gained and lost dominance score method for risk prioritization in FMEA with Fermatean fuzzy information

Failure mode and effect analysis (FMEA) is one of the most effective means for potential systematic risk assessment in a real work environment. Nevertheless, the traditional FMEA approach has been extensively criticized for many deficiencies in coping with risk evaluation and prioritization problems under inter-uncertain environments. To overcome the limitations, in this paper, a synthesized risk priority calculation framework is proposed for FMEA by combining the gained and lost dominance score (GLDS) method, the combination ordered weighted averaging (C-OWA) operator, and Fermatean fuzzy set (FFS). Firstly, we use FFS to express the experts’ uncertain risk evaluation information which can depict the fuzziness and ambiguity of the information. Secondly, the C-OWA operator combined with FFS is introduced to build the group risk matrix which can provide a more reasonable risk analysis result. Then, the developed GLDS method with FFS is presented to calculate the risk priority of each failure mode which takes both individual and group risk attitudes into consideration. Finally, a medical device risk analysis case is introduced to demonstrate the proposed FMEA framework. We also perform comparison analyses to confirm the effectiveness and rationality of the hybrid risk prioritization framework for FMEA under a complex and uncertain situation.

Failure Mode and Effect Analysis Based on Probabilistic Linguistic Preference Relations and Gained and Lost Dominance Score Method.

Failure mode and effect analysis (FMEA) is a widely used reliability management technology to evaluate the risk of potential failures in a system, product, or service. Nevertheless, the normal risk priority number (RPN) method has been extensively criticized for many deficiencies in practical applications. To overcome the drawbacks of traditional FMEA, plenty of methods have been suggested in previous studies. But majority of them evaluated the risk factors of each failure mode directly and cannot take group and individual risk attitudes into account. In this article, we put forward a new FMEA approach integrating probabilistic linguistic preference relations (PLPRs) and gained and lost dominance score (GLDS) method. The PLPRs are adopted to describe the risk evaluations of experts by pairwise comparison of failure modes. An extended GLDS method is introduced to derive the risk ranking of failure modes considering both group and individual risk attitudes. Moreover, a two-step optimization model is proposed to determine the weights of risk factors when their weighing information is unknown. Finally, a load-haul-dumper machine risk analysis case is presented to demonstrate the proposed FMEA. It is shown that the approach being proposed in this study provides a practical and effective way for risk evaluation in FMEA.

A novel FMEA approach for submarine pipeline risk analysis based on IVIFRN and ExpTODIM-PROMETHEE-II

The failure mode and effects analysis (FMEA) approach based on TODIM has been widely applied in risk analysis in several fields. To fill the gap in relevant research in the field of submarine pipeline risk analysis and to address possible problems in the classical TODIM method, a novel FMEA model is proposed in this paper. Firstly, an improvement to the existing interval-valued intuitionistic fuzzy rough number (IVIFRN) theory is made and used to collect expert opinions better to consider individual and group uncertainty. Secondly, ExpTODIM, which is more in line with prospect theory, is combined with the PROMETHEE-II method. And it also incorporates weight calculation methods such as the maximizing deviation method for ranking failure modes. Thirdly, the exponential entropy weight method based on the improved IVIFRN theory is combined with the analytical hierarchical process (AHP) for application in fuzzy comprehensive evaluation to complete the calculation of risk values. Fourthly, the proposed novel FMEA approach is applied to the risk analysis of the submarine pipeline to obtain its failure mode ranking and risk value results. Comparisons with other methods in concept and example analysis show that the proposed method is relatively reliable and has certain advantages over other current FMEA methods based on MCDM. In particular, the proposed method overcomes the theoretical problems of the classical TODIM and is more consistent with logic. It also improves the accuracy and comprehensiveness of analysis results, which can provide a valid and accurate reference for the practical engineering of submarine pipelines.