Failure mode and effects analysis based on D numbers and TOPSIS

Bian, Tian; Zheng, Haoyang; Yin, Likang; Deng, Yong

doi:10.1002/qre.2268

Cited by 169 publications

(85 citation statements)

References 52 publications

(122 reference statements)

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“…Furthermore, we convert the risk evaluation values into rough interval numbers based on Equations (7) to (12). Then, the risk evaluation matrix is constructed.…”

Section: Illustration Of the Proposed Fmea Methodsmentioning

confidence: 99%

“…In most application cases, the three risk factors are assumed to be equally weighted, and the relative importance is not taken into consideration, which may lead to inaccurate results in risk assessment process. 12 Bowles 13 once presented that the values of S, O, and D are considered as an ordinal scale but the calculation of multiplication and division is meaningless on the ordinal scale. Risk priority numbers have many duplicate numbers because various combinations of S, O, and D can produce the same value of RPNs, while the potential risks may be totally different.…”

Section: Introductionmentioning

confidence: 99%

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An interval probability‐based FMEA model for risk assessment: A real‐world case

Ouyang

Zheng

Zhu

et al. 2019

Quality & Reliability Eng

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Failure mode and effect analysis (FMEA) is an effective quality tool to eliminate the risks and enhance the stability and safety in the fields of manufacturing and service industry. Nevertheless, the conventional FMEA has been criticized for its drawbacks in the evaluation process of risk factors or the determination of risk priority number (RPN), which may lead to inaccurate evaluation results. Therefore, in this paper, we develop a novel FMEA method based on rough set and interval probability theories. The rough set theory is adopted to manipulate the subjectivity and uncertainty of experts' assessment and convert the evaluation values of risk factors into interval numbers. Meanwhile, the interval exponential RPN (ERPN) is used to replace the traditional RPN due to its superior properties, eg, solving the problems of duplicate numbers and discontinuity of RPN values. Furthermore, an interval probability comparison method is proposed to rank the risk priority of each failure mode for avoiding the information loss in the calculation process of RPN. Finally, a real case study is presented, and the comparison analysis among different FMEA methods is conducted to demonstrate the reliability and effectiveness of the proposed FMEA method.

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“…Furthermore, we convert the risk evaluation values into rough interval numbers based on Equations (7) to (12). Then, the risk evaluation matrix is constructed.…”

Section: Illustration Of the Proposed Fmea Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An interval probability‐based FMEA model for risk assessment: A real‐world case

Ouyang

Zheng

Zhu

et al. 2019

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

“…Many math tools, such as AHP [37][38][39][40], fuzzy sets [41][42][43], D numbers [44][45][46][47], evidential reasoning [30,[48][49][50][51], and rough sets [52], are adopted with wide applications. In this section, the main concepts about the Dempster-Shafer (D-S) evidence theory are reviewed.…”

Section: Dempster-shafer Theory Of Evidencementioning

confidence: 99%

Evidential Model Validation under Epistemic Uncertainty

Deng

2018

Mathematical Problems in Engineering

Self Cite

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This paper proposes evidence theory based methods to both quantify the epistemic uncertainty and validate computational model. Three types of epistemic uncertainty concerning input model data, that is, sparse points, intervals, and probability distributions with uncertain parameters, are considered. Through the proposed methods, the given data will be described as corresponding probability distributions for uncertainty propagation in the computational model, thus, for the model validation. The proposed evidential model validation method is inspired by the idea of Bayesian hypothesis testing and Bayes factor, which compares the model predictions with the observed experimental data so as to assess the predictive capability of the model and help the decision making of model acceptance. Developed by the idea of Bayes factor, the frame of discernment of Dempster-Shafer evidence theory is constituted and the basic probability assignment (BPA) is determined. Because the proposed validation method is evidence based, the robustness of the result can be guaranteed, and the most evidence-supported hypothesis about the model testing will be favored by the BPA. The validity of proposed methods is illustrated through a numerical example.

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“…In addition, a combination of multicriteria decision‐making techniques such as analytical hierarchy process (AHP) and subjective probabilistic risk assessment methods has been used by the literatures . Other techniques in probabilistic risk assessment techniques such as simple risk matrix, FMEA, and reliability block diagrams have also engaged different elicitation and aggregation procedures by the literatures …”

Section: Introductionmentioning

confidence: 99%

Introducing a heuristic approach to enhance the reliability of system safety assessment

Yazdi

2019

Quality & Reliability Eng

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Probabilistic risk assessment techniques are the important tools which can considerably improve the safety performance of the studied system and reduce the risk to an acceptable level. Typically, decision‐making process is an important part of risk assessment methods that accordingly bring the ambiguity inside. Decision makers as experts commonly express their subjective opinions about the occurrence of the root events in order to obtain the probability of the undesired event. Subsequently, the critical root events are identified, and possible intervention is performed to reduce the probability of the critical events. However, the serious point is the viability of the obtained probabilities and priority ranking of the critical events. In this study, a heuristic optimization model of linear mathematical programming using triangular intuitionistic fuzzy number (TrIFN) is proposed to obtain the feasible, optimum, and reliable results compared with available methods. The Spearman correlation is performed to examine the reliability and behavior of the proposed model. In order to show the effectiveness of the proposed approach, it is applied on a real case study. The application of the model confirms its robustness to prioritize critical root events over the conventional one.

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Failure mode and effects analysis based on D numbers and TOPSIS

Cited by 169 publications

References 52 publications

An interval probability‐based FMEA model for risk assessment: A real‐world case

An interval probability‐based FMEA model for risk assessment: A real‐world case

Evidential Model Validation under Epistemic Uncertainty

Introducing a heuristic approach to enhance the reliability of system safety assessment

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