A risk assessment approach for failure mode and effects analysis based on intuitionistic fuzzy sets and evidence theory

Guo, Jian

doi:10.3233/ifs-151809

Cited by 48 publications

(24 citation statements)

References 35 publications

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“…Tough evidence theory has some open issues, such as conflict management [16,58], dependent evidence combination [53] and determination of basic probability assignment [28], it has a wide applications like fault diagnosis [27], supplier chain management [13,17], decision making [15,22,31,38] and risk evaluation which matters a lot in reality [20,24,30], due to its efficiency to model and fuse uncertain information. The following is some basic concepts of D-S evidence theory.…”

Section: Dempster-shafer Evidence Theorymentioning

confidence: 99%

A new belief Markov chain model and its application in inventory prediction

Jiang

2017

International Journal of Production Research

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Markov chain model is widely applied in many fields, especially the field of prediction. The classical Discrete-time Markov chain(DTMC) is a widely used method for prediction. However, the classical DTMC model has some limitation when the system is complex with uncertain information or state space is not discrete. To address it, a new belief Markov chain model is proposed by combining Dempster-Shafer evidence theory with Markov chain. In

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Section: Dempster-shafer Evidence Theorymentioning

confidence: 99%

A new belief Markov chain model and its application in inventory prediction

Jiang

2017

International Journal of Production Research

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“…In order to avoid an individual bias, FMEA activity is usually performed based on a team and should be regarded as a group decision behavior [10]. Therefore, FMEA activity can be considered as multi-criteria group decision making (MCGDM) problems [15]. Generally, the implementation of group-based FMEA consists of three stages, which includes failure modes' evaluation, determination of the weights of risk factors, and failure modes ranking [16].…”

Section: Introductionmentioning

confidence: 99%

“…Failure modes evaluation involves expression and aggregation expert preferences. To express precisely and completely experts' preferences, many alternative methods, which incorporate fuzzy set theory and its extended forms [7,15,[17][18][19], have been introduced into FMEA to deal with the uncertainty and ambiguity of experts' judgment. However, no or little attention has been paid to the way of aggregating experts' preferences, which usually impact on the final ranking of failure modes.…”

Section: Introductionmentioning

confidence: 99%

Risk Assessment for Failure Mode and Effects Analysis Using the Bonferroni Mean and TODIM Method

et al. 2019

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As a safety and reliability analysis technique, failure mode and effects analysis (FMEA) has been used extensively in several industries for the identification and elimination of known and potential failures. However, some shortcomings associated with the FMEA method have limited its applicability. This study aims at presenting a comprehensive FMEA model that could efficiently handle the preference interdependence and psychological behavior of experts in the process of failure modes ranking. In this model, a linguistic variable expressed by the interval-valued Pythagorean fuzzy number (IVPFN) is utilized by experts to provide preference information with regard to failure modes’ evaluation and risk factors’ weight. Then, to depict the interdependent relationships between experts’ preferences, the Bonferroni mean operator is extended to IVPFN to aggregate the experts’ preference. Subsequently, an extended TODIM approach in which the dominance degree of failure modes is calculated by grey relational analysis is utilized to determine the risk priority of failure modes. Finally, a practical example concerning the risk assessment of a nuclear reheat valve system is provided to demonstrate the effectiveness and feasibility of the presented method. In addition, a sensitivity analysis and comparison analysis are conducted, and the results show that the preference interdependence and psychological behavior of experts have an important effect on the risk priority of failure modes.

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“…The GBM operator has a prominent characteristic that can easily capture the interrelationships among input arguments [9]. Therefore, it is significant to integrate the experts' preferences into a comprehensive preference by adopting the GBM operator that depicts the interdependent relationships between the experts' preferences before determining the risk priority of failure modes.On the other hand, group-based FMEA risk assessment is essentially a multi-criteria group decision-making problem [10]. Many different approaches based on group decision-making, which assume that FMEA team experts have reached a consensus before ranking the failure modes, have been presented by researchers and practitioners to improve the reliability of the conventional FMEA method.…”

mentioning

confidence: 99%

“…On the other hand, group-based FMEA risk assessment is essentially a multi-criteria group decision-making problem [10]. Many different approaches based on group decision-making, which assume that FMEA team experts have reached a consensus before ranking the failure modes, have been presented by researchers and practitioners to improve the reliability of the conventional FMEA method.…”

mentioning

confidence: 99%

Failure Mode and Effects Analysis Considering Consensus and Preferences Interdependence

Zhu

Wang

2018

Algorithms

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Failure mode and effects analysis is an effective and powerful risk evaluation technique in the field of risk management, and it has been extensively used in various industries for identifying and decreasing known and potential failure modes in systems, processes, products, and services. Traditionally, a risk priority number is applied to capture the ranking order of failure modes in failure mode and effects analysis. However, this method has several drawbacks and deficiencies, which need to be improved for enhancing its application capability. For instance, this method ignores the consensus-reaching process and the correlations among the experts' preferences. Therefore, the aim of this study was to present a new risk priority method to determine the risk priority of failure modes under an interval-valued Pythagorean fuzzy environment, which combines the extended Geometric Bonferroni mean operator, a consensus-reaching process, and an improved Multi-Attributive Border Approximation area Comparison approach. Finally, a case study concerning product development is described to demonstrate the feasibility and effectiveness of the proposed method. The results show that the risk priority of failure modes obtained by the proposed method is more reasonable in practical application compared with other failure mode and effects analysis methods.Algorithms 2018, 11, 34 2 of 24 of limitations. Numerous alternative methods have been presented in the literature to handle some of these drawbacks [2], which could explain why focusing on these shortcomings can improve the performance of traditional FMEA. However, many practical application cases have suggested that the ranking results determined by these alternative methods are not reliable and suffer from drawbacks in some situations. The main reasons are the assumption that the experts' preferences are independent and the team of experts have reached a consensus before aggregating the experts' preferences. Therefore, it is worth studying the problem of how to determine effectively the risk priority of failure modes without considering these two assumptions.It is very important for an FMEA team to adopt a suitable aggregation method to aggregate the experts' preferences into a collective evaluation matrix before determining the failure modes ranking. Many aggregation methods that assume that experts' preferences are independent have been applied to aggregate the experts' preferences. In practice, the FMEA team experts come from different departments or industries, and their subjective preferences, which are often influenced by social, power, knowledge, and other factors, usually indicate some interdependent characteristics. On the one hand, the geometric Bonferroni mean (GBM) initially proposed by Xia et al. [9] is an efficient operator to deal with the aggregation of interdependent arguments. The GBM operator has a prominent characteristic that can easily capture the interrelationships among input arguments [9]. Therefore, it is significant to integrate the experts' preferences...

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A risk assessment approach for failure mode and effects analysis based on intuitionistic fuzzy sets and evidence theory

Cited by 48 publications

References 35 publications

A new belief Markov chain model and its application in inventory prediction

A new belief Markov chain model and its application in inventory prediction

Risk Assessment for Failure Mode and Effects Analysis Using the Bonferroni Mean and TODIM Method

Failure Mode and Effects Analysis Considering Consensus and Preferences Interdependence

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