Traditional Failure Mode And Effects Analysis Research Articles

Sustainability risk assessment in manufacturing: A Life Cycle Assessment-based Failure Mode and Effects Analysis approach

This study introduces a methodology integrating Life Cycle Assessment (LCA) with Failure Mode and Effects Analysis (FMEA) to identify and assess environmental sustainability risks in manufacturing processes in alignment with the Corporate Sustainability Reporting Directive (CSRD) and European Sustainability Reporting Standards (ESRS). In contrast to existing top-down approaches at sector and company level, this methodology specifically addresses the challenge of transitory sustainability risks in manufacturing, driven by rapid changes in regulatory frameworks and market expectations, by providing a robust and adaptable framework for comprehensive, bottom-up risk assessment directly on the manufacturing shop floor. Through a detailed literature review, the paper identifies gaps in current risk assessment methods and proposes a novel approach that enhances traditional FMEA by incorporating LCA results, thereby facilitating a comprehensive, quantitative analysis of sustainability risks. The practical application of this methodology is demonstrated through a case study, underscoring its effectiveness in real-world settings and potential for broader applications in sustainable manufacturing. The results presented in the paper also include quantitative sustainability performance benchmarks for the EU28 industrial sector, offering a strategic tool for manufacturers to improve sustainability practices, align operations with environmental standards, and mitigate transitory sustainability risks. This research contributes to the sustainable evolution of manufacturing, providing a methodological foundation for understanding and managing sustainability risks.

Failure Mode and Effects Analysis of Accessibility in Frigid Zone Campus Buildings

With the progress of China’s economy, there is an increasing focus on accessibility systems. Enhancements to accessibility infrastructure are being implemented in all types of structures, with a particular focus on educational institutions such as college campuses. This research was carried out to examine the campus accessibility system of Northeastern University’s Nanhu and Hunnan Campuses in Shenyang City, China, using failure mode and effects analysis (FMEA) as a methodology and using incorporating fuzzy control, which overcomes the limitations of traditional FMEA. Fuzzy-FMEA integrates the fuzzy linguistic assessment to assist the analysis process, in contrast to standard analysis which mainly relies on subjective judgment. Through calculations, it is known that ramps, barrier-free toilets, and barrier-free entrances are the items with the highest failure modes. The construction of the campus accessible environment needs to prioritize solving the problems of these facilities. The research results also found that there is a lack of research specifications for accessible environments in China’s cold regions, and universal specifications are not fully suitable. The accessibility of the new campus was improved and management was improved, reflecting the school’s increased awareness of accessible environment construction in recent years. However, there are still many common problems in the old and new campuses, proving that they are not aware of the importance and urgency of improving these problems. In addition, the construction of barrier-free facilities in the administrative office buildings where management and faculty are located on both campuses is generally in good condition. The accessible design of dormitories and canteens commonly used by students is often ignored. Moreover, the rough detailing in many buildings prevents these facilities from being used properly. These are the challenges faced by the Northeastern University in building an accessible environment.

Analyzing the risk of the ammonia storage facility using extended FMEA model based on probabilistic linguistic GLDS method with consensus reaching

Due to the energy crisis, ammonia stands out as an efficient hydrogen energy carrier. However, liquid ammonia's nature of toxicity, corrosiveness, volatility, flammability, etc., make its storage highly risky. It is essential to analyze the risk of the ammonia storage facility for its safety improvement. Failure mode and effect analysis (FMEA) has been a popular risk analysis method due to its ease of implementation. However, traditional FMEA is insufficient to model the uncertainty of risk assessment information, enhance the consensus among experts, aggregate experts' risk evaluation information, and derive reliable risk analysis results. To address these limitations, we introduce a hybrid probabilistic linguistic FMEA framework to analyze failure risk in the ammonia storage facility. First, the probabilistic linguistic terms (PLTs) are introduced to express uncertain risk evaluation information of failure modes. Second, a risk acceptability-based consensus-reaching process is introduced to measure experts' consensus on each failure mode's risk and improve their consensus. Then, a consensus degree-based ordered weighted averaging operator is introduced to fuse experts' risk evaluation information, where experts' weight is determined by their consensus degree. Furthermore, the gained and lost dominance score method is improved on the basis of the expected value of the PLTs to prioritize failure modes' risk. Finally, a case study of the risk analysis for the ammonia storage facility is performed to show the efficiency of the extended FMEA. The case study indicated that condenser cooling water pump failure and failure of level transfer are the top two failure modes and have risk scores of 0.126 and 0.091, respectively.

A novel method for weighting decision makers for failure mode and effect analysis under intuitionistic fuzzy environment

AbstractThis study introduces an innovative method to implement Failure Mode and Effect Analysis (FMEA) in an Intuitionistic Fuzzy (IF) environment and targets inherent limitations of traditional FMEA, particularly its reliance on precise numerical values that can lead to human subjectivity and compromise accuracy of analysis. The primary aim is to increase the objectivity in evaluating Failure Modes (FMs) in industrial settings, thus enhancing safety and quality assessments. The novel approach integrates the Measurement Alternatives and Ranking according to the Compromise Solution (MARCOS) method within an IF framework. An implementation at a defense industry company is performed for evaluation of the FMs by four Decision Makers (DMs), each possessing different levels of experience and system knowledge. A key innovation of this study is the unique weighting methodology applied to DMs, based on predefined rules, which acknowledges and quantifies their varied expertise and insights. Our methodology significantly contributes to the study's practicality and significance in ranking FMs, emphasizing its effectiveness, robustness, and accuracy. The findings reveal that this method greatly reduces the subjectivity found in traditional FMEA, also providing a more detailed and reliable assessment. This is achieved by adapting the analysis to the specific knowledge levels of the DMs, leading to more dependable and objective quality evaluations in industrial processes. The application of this method in the real‐world setting demonstrates its practical relevance and potential for wider adoption.

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 method based on q-rung orthopair fuzzy cognitive map and TOPSIS method for failure mode and effect analysis considering risk causal relationships

As a preventive maintenance technique, failure mode and effect analysis (FMEA) is widely applied to determine and eradicate possible failures. In recent years, many FMEA methods have been developed to address the limitations of traditional FMEA methods in failure mode (FM) assessment, weight calculation, and FM prioritization in complex uncertain environments. However, due to the complexity of equipment, systems, services, etc., there is an inevitable influence on causal relationships between FMs, which is rarely considered by existing methods. Therefore, a novel FMEA method, which integrates the technique for order preference by similarity to the ideal solution (TOPSIS) of q-rung orthopair fuzzy set (q-ROFS) and q-rung orthopair fuzzy cognitive map (q-ROFCM), is proposed for reasoning the relationships between FMs. The design of the novel method, named q-ROFCM FMEA, involves three stages. The q-ROFS is applied to evaluate the risk factors for capturing more ambiguous information in the first stage. The q-ROFCM is proposed and the causal relationship between FMs is integrated to describe the impact of the relationships on the evaluation results through the dynamic behavior of FMs in the second stage. In the third stage, the TOPSIS method is applied to determine the priority ranking of FMs based on more reliable similarity measurement results of the projection measure. The proposed new method is applied to the FM risk assessment of power units in the railway turnout system. The competitiveness and effectiveness of the proposed method have been demonstrated by comparison with other classic methods and sensitivity analysis.

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.

Risk assessment based on failure mode and effects analysis (FMEA) and WASPAS methods under probabilistic double hierarchy linguistic term sets

Failure mode and effects analysis (FMEA) is regarded as an efficient tool in identifying and eliminating system failures and important tool in safety and reliability analysis. However, the traditional FMEA method has some drawbacks, for example, in the process of risk assessment, traditional FMEA method usually ignores the influence factors of weight. Simultaneously, when represent risk assessment information, crisp numbers and fuzzy sets cannot represent complex linguistic information forms accurately. Moreover, the traditional FMEA method ranks risk failures through simple product. Thus, based on these disadvantages, this paper aims to present an improved FMEA method combines with the Weighted Aggregates Sum Product Assessment (WASPAS) method and weight determination method to address the risk assessment problems under probabilistic double hierarchy linguistic (PDHL) environment. In assessment process, the probabilistic double hierarchy linguistic term sets (PDHLTSs) can indicate the probability distribution as a linguistic tool to describe the information reasonably and precisely. The contribution s of this paper can be incarnated as below. Firstly, the PDHLTSs are utilized to provide the linguistic information of failure modes and construct risk evaluation model in risk assessment. Secondly, FMEA method is combined with WASPAS method to overcoming some drawbacks existing in traditional FMEA such as without consideration of the weight of three aspects in Severity (S), Occurrence (O) and Detection (D) and too simple to evaluate the risk factors using Risk Priority Number (RPN) values. Meanwhile, in response to the issue of traditional FMEA method neglecting expert weights, weight determination method is established based on the SNA method under the PDHL context. Additionally, the FMs are ranked through the multi-attribute decision-making method WASPAS, and the final risk ranking result is obtained. Finally, the proposed method is applied to the marine risk assessment faced in the development of marine economy. Based on the assessment results, suggestions are given for the risk problems. In addition, we also carried out sensitivity analysis and accuracy analysis on the evaluation results. At the same time, comparison analysis with existing methods is given to illustrate the superiority of the proposed method.

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 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.

Assessing the impact of healthcare service risks on healthcare demand under evolving economic and social structures: An improved GLDS decision making method considering risk attitudes

Faced with backward and uneven economic conditions and an irrational and increasingly inequitable social structure, healthcare disparities are widening and the healthcare inequalities are expanding. This can lead to an increasing public demand for healthcare services, posing unprecedented risk, such as resource shortages, quality deterioration and rising healthcare costs. By identifying, managing, and optimizing the aforementioned risks within the healthcare system, it helps the healthcare system better address the challenges of expanding healthcare demands under evolving economic and social structure. To achieve this, this paper employs failure mode and effects analysis (FMEA) to identify severe failure modes in healthcare services. However, traditional FMEA methods possess limitations in assessing the dynamics of trust relationships and interaction effects among experts within social networks. Therefore, this paper proposes an improved gained and lost dominance score (GLDS) based social network decision-making method considering risk attitudes for FMEA to prioritize failure modes in healthcare services. First, transform linguistic risk assessment information from an interdisciplinary team of experts based on probabilistic linguistic term sets handle uncertain efficiently. Meanwhile, experts’ influence is reflected by an extended PageRank algorithm with trust degrees based on social networks analysis. Next, an improved GLDS method considering the differentiated risk attitudes of individuals and groups is developed to prioritize failure modes. Especially, to solve the problems of soft preference and incomparability relation among failure modes, which are ignored by the traditional GLDS method, a conflict analysis is established to determine the preference, indifference, and incomparability relations. Finally, the practicality of the proposed method is illustrated by the healthcare service risk case in the emergency department and the effectiveness of the developed method is validated by comparison and sensitivity experiments. The results suggest that failure to update labels showing patient severity promptly is the most dangerous. In addition, it would be more economically efficient to analyze the non-comparable failure modes facing different risk factors.

Probabilistic double hierarchy linguistic risk analysis based on failure mode and effects analysis and S-ARAS method

Failure mode and effects analysis (FMEA) is regarded as useful and efficient tool in identifying and eliminating system failures and important means in safety and reliability analysis. It is widely utilized in diverse areas for improving and enhancing the performance of systems. However, in the procedure of risk assessment, traditional FMEA method usually ignores the influence of weights. Simultaneously, when represent risk assessment information, fuzzy and complex information cannot be represented accurately. Besides, psychological behaviors of decision-makers cannot be considered. Thus, this paper aims to present a new method based on FMEA and S-additive ratio assessment (S-ARAS) method to address the marine risk assessment problems under probabilistic double hierarchy linguistic (PDHL) environment. Firstly, the probabilistic double hierarchy linguistic term sets (PDHLTSs) are utilized to express the evaluation information of failure modes. The expert evaluation information of failure modes and subjective weight is also assessed by using PDHLTSs. The PDHLTSs can describe the information more reasonably and precisely. Secondly, FMEA method is used to evaluate these alternatives from three aspects of Severity (S), Occurrence (O) and Detection (D). Then, the bounded rationality of decision-makers (DMs) is considered. So, the prospect theory and ARAS method is combined to evaluate the risk problems. Meanwhile, comprehensive weight determination method is established based on the social network analysis (SNA) and The Integrated Determination of Objective CRIteria Weights (IDOCRIW) method under PDHL environment. The integrated method is more flexible in determining the importance of failure modes and avoids the randomness of subjective evaluation. Finally, the newly proposed method is applied to address the marine risk assessment problem. Sensitivity analysis of parameter and weight methods is carried out. Furthermore, comparison analysis is given to illustrate the superiority of the presented method.

A holistic failure modes and effects analysis for university plastic injection laboratory under Bayesian Network

University Research & Development (R&D) laboratories are facilities used for research and manufacturing activities in engineering and basic science fields. Various filling materials are used in plastic R&D laboratories to improve the mechanical properties of thermoplastics or to recycle waste materials. The use of filling materials causes changes in plastic production parameters. This study dealt with the risks arising from extrusion, injection and shredder in a plastic injection laboratory, which led to the inability to manufacture the appropriate product under a holistic methodology. The proposed holistic methodology merges the concepts of failure mode and effects analysis (FMEA), fuzzy best-worst method (FBWM), and a rule-based three-hierarchical Bayesian network (RB-THBN). First, 15 root risks have been identified under a three-stage hierarchy with the support of laboratory decision makers. Then, questionnaires were created using the FMEA concept. The importance values (degree of belief-DoB) of three different FMEA parameters (severity, occurrence and detection) by experts for each risk have been calculated with FBWM. And finally, the RB-THBN has been created, and the final risk values have been computed using the linear utility function for the network. The system has been analyzed via a Bayesian modeling software. A validity test and a control measures planning has also been executed. According to the results, the highest root risk has been determined as “E2-The prepared mixture is not homogeneous” with a crisp risk score of 0.639559. In addition, “Failure due to extrusion” has been determined as the highest risk category with a score of 0.564059. It was discussed in detail that these risks arise from homogenization and how the used mixture should be homogenized. A comparative study among traditional FMEA, FMEA extended under FBWM and the proposed approach is performed to observe the differences in final ranking of root risks and to highlight the arguments that strengthen the advantages of the proposed method over the other two.

Risk assessment of bolt-gasket-flange connection (BGFC) failures at hydrogen transfer stations based on improved FMEA

Failure Modes and Effects Analysis (FMEA) is an engineering technique widely used to identify, evaluate, and eliminate known or potential failures, problems, and errors and inform risk management decisions. Traditional FMEA uses the risk priority number (RPN) to identify potential failure modes and their levels. However, it displays shortcomings in determining risk factor weights, ranking risk priorities of failure modes, and dealing with uncertainties during the risk assessment process. To address these issues, this paper proposed an improved FMEA risk assessment method by integrating FMEA, the analytic hierarchy process (AHP), the technique for order preference by similarity to an ideal solution (TOPSIS), and the cloud model (CM) to determine a more reasonable failure mode level. First, the CM was used to improve the FMEA framework structure to comprehensively assess ambiguity and randomness during the evaluation process. Second, the cloud weights of the risk factor severity (S), occurrence (O), and detectability (D) were calculated using AHP and CM to determine the relative risk factor importance. Then, the cloud distance measurement algorithm and TOPSIS were combined into the TOPSIS-CM risk ranking method to objectively identify the failure modes requiring improvement. Finally, a case study of a flange connection system at a hydrogen blending transmission site in northern China was conducted to verify the adaptability of the proposed method in failure mode assessment. Compared with traditional FMEA methods, the decision scores obtained using the improved FMEA were more discriminating and distinguished the risk of each failure mode, providing more accurate information to decision makers (DMs).

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.

A hybrid generalized TODIM based risk prioritization method for failure mode and effect analysis with linguistic Z-numbers

The traditional failure mode and effect analysis (FMEA), as an effective risk analysis technique, has several limitations in the uncertainty modeling and the weights determination of the risk indicators. This paper aims to propose a hybrid risk prioritization method simultaneously considering the characteristics of the reliability associated with the FMEA team members’ evaluation information and their psychological behavior to enhance the performance of the traditional FMEA model. The hybrid risk prioritization method is developed based on the generalized TODIM method and the weighted entropy measure with the linguistic Z-numbers (LZNs). First, the LZNs are adopted to depict the FMEA team members’ cognition information and the reliability of these information. Second, a weighted entropy measure based on the fuzzy entropy and the LZNs is developed to obtain the risk indicators’ weights. Finally, the generalized TODIM method with the LZNs is constructed to obtain the risk priority orders of failure modes, which can effectively simulate the FMEA team members’ psychological character. The applicability and effectiveness of the proposed risk prioritization method is validated through an illustrative example of an integrated steel plant. The results of sensitivity analysis and comparative analysis indicate that the proposed hybrid risk prioritization method is effective and valid, and can get more accurate and practical risk ranking results to help enterprises formulate accurate risk prevention and control plans.

A novel failure mode and effect analysis method with spherical fuzzy entropy and spherical fuzzy weight correlation coefficient

As a proactive reliability analysis technique, the purpose of failure mode and effect analysis (FMEA) is the detection of all failures and their effects within a system, and the determination of the causes of these failures. However, the traditional FMEA method shows some important drawbacks regarding failure mode evaluations, risk factor weights and risk priority ranking, etc. This paper aims to develop a novel FMEA to improve the performance of FMEA by using spherical fuzzy sets (SFSs) and spherical fuzzy weight correlation coefficient (SF-WCC). The method mainly includes three stages. The SFS is applied to capture risk evaluation information because it comprehensively considers the information expression in different scenarios in the first stage. A spherical fuzzy projection model is introduced to calculate the expert weights objectively for reducing the information difference caused by experience, psychology and other cognitive factors of experts in the second stage. Meanwhile, the spherical fuzzy entropy is defined to determine the incomplete weights of risk factors by constructing an optimization model in the second stage. The SF-WCC is proposed to prioritize the failure modes to avoid the prioritization controversy of failure modes by using different distance formulas in the third stage. In the experiment, a practical example is presented to illustrate the applicability and effectiveness of the proposed method, and results show that the proposed approach offers a reliable tool for practical FMEA problems. The conclusion is that the proposed method is applicable to multiple actual scenarios as a reliable risk assessment technology.

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.

FMEA Assessment Under Heterogeneous Hesitant Fuzzy Preference Relations: Based on Extended Multiplicative Consistency and Group Decision Making

Failure mode and effects analysis (FMEA) is a reliability analysis method that analysis all possible failure modes for each product in a system and all possible effects of failure modes on the system, to classify each failure mode and to propose solutions and preventative measures. It is undeniable that the traditional FMEA method has been widely criticized for its simple scoring and single algorithm. To improve the usability of FMEA, hesitation fuzzy preference relation sets (HFPRs) based on hesitant fuzzy sets have been introduced into FMEA research because of their good fuzziness and uncertainty properties. Most existing consistency-based algorithms for HFPR processing, however, do not consider the possible coherence deviation of the reluctant fuzzy set itself, which includes multiplicative consistency (MC) theory, which will result in the reduced accuracy of results from such algorithms, in addition to not supporting group decision-making well in heterogeneous environments. At the same time, when building a group consensus, constantly adjusting HFPRs through group decision feedback can easily lead to conservative or radical scoring by experts. Therefore, an excellent hybrid FMEA assessment method is studied in this paper. In this approach, an extended multiplicative consistency equation is constructed by extending the applicability of MC in the treatment of HFPR, and on this basis, a mathematical model with the ability to deal with sets of heterogeneous fuzzy preference relations (H-HFPRs) is constructed. Lastly, based on the predictability principle of the occurrence level (O), a scoring correction algorithm is constructed based on group consensus theory to reduce the conservative or aggressive bias of the expert group in the results. The new method was used in the risk assessment of Change oilfield subsea pipeline engineering, and the results were compared with several existing methods to verify the effectiveness and advancement of the proposed method.