Fuzzy Risk Evaluation in Failure Mode and Effects Analysis Using a D Numbers Based Multi-Sensor Information Fusion Method

Deng, Xinyang; Jiang, Wen

doi:10.3390/s17092086

Cited by 47 publications

(40 citation statements)

References 62 publications

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“…First, since the weights of risk factors and the rough numbers of failure modes have been obtained in stages 2 and 3, the interval ERPNs of failure modes can be calculated based on Equation (33). The results are listed in Table 7.…”

Section: Illustration Of the Proposed Fmea Methodsmentioning

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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Section: Illustration Of the Proposed Fmea Methodsmentioning

confidence: 99%

“…33 What is more, failure modes with higher RPN values are considered to be more serious and should be given a higher rating of improving. A numeric scale from 1 to 10 is applied to evaluate the three risk factors.…”

Section: The Exponential Rpnmentioning

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

“…Therefore, to overcome these existing shortcomings, Deng proposed a generalization of the evidence theory referred to as the D‐number to enhance its capability and flexibility in dealing with uncertain information. The D‐number relaxes the exclusiveness hypothesis of the FOD and the completeness constraint of the mass function . Some basic concepts regarding the D‐number are given as follows.…”

Section: Preliminariesmentioning

confidence: 99%

A novel framework for the reliability modelling of repairable multistate complex mechanical systems considering propagation relationships

Gao

Wang

Gao

et al. 2018

Quality & Reliability Eng

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Due to the propagation, amplification, and concatenation in a failure process, the reliabilities of repairable multistate complex mechanical systems (RMCMSs) may be affected by a significant fluctuation due to a small exception associated with a reliability indicator. Focused on the problems arising from the lack of propagation relationships among fault modes, functional components, and failure causes in conventional reliability models, a novel framework for reliability modelling is proposed to comprehensively analyse the reliabilities of RMCMSs. First, the reliability models are abstracted as weighted and directed networks with five layers. Second, an improved failure mode and effects analysis (IFMEA) method combined with the D‐number method and VIKOR approach is presented to determine the importance of reliability nodes. Third, a cut set of the reliability model is generated by any exception of a reliability indicator by considering the propagation relationships, and the reliability sensibility index is defined to characterize the fluctuations in system reliability. The effectiveness of the proposed framework is demonstrated in an actual reliability modelling application. As an intuitive method, the proposed framework inherits the advantages of conventional models but overcomes the drawbacks of these existing methods. Therefore, this method can be flexibly and efficiently used in the reliability modelling of RMCMSs. Moreover, the approach provides a foundation for comprehensive and dynamic reliability analysis and the failure mechanism mining of RMCMSs, and it can be used in other engineering applications.

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“…On the one hand, under the complex monitoring environment and unbalanced sensor sensitivities, environmental noise and man-made interference seriously affect the precision and reliability of sensor information [4]. On the other hand, due to the limited accuracy of sensors, several sensors cannot measure the real target information.…”

Section: Introductionmentioning

confidence: 99%

A Robust DS Combination Method Based on Evidence Correction and Conflict Redistribution

Chen

Tian

2018

Journal of Sensors

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To eliminate potential evidence conflicts, an effective and accurate DS combination method is addressed in this paper. DS evidence theory is an outstanding information fusion approach with valid uncertainty treatment. Nevertheless, there are some limitations of the usage of the DS evidence theory. On the one hand, due to the complexity of a combat measurement environment and the inconsistency of sensor capabilities, sensor sources have enormous uncertainty, which would inevitably cause conflicts for evidence combination. On the other hand, DS combination rule realizes the unity property of fusing results with a compulsive normalization, which unavoidably leads to conflicting situations. To solve the possible evidence conflicts in a multisensor fusion system, we raise a robust DS combination method based on evidence correction and conflict redistribution. Firstly, two corrected indexes-the reliability index and consistency index-are separately addressed with the introduction of the Matusita distance function and closeness degree function. After the evidence modification based on two correction indexes, the conflicts caused by unreliable sensor sources are solved. Then, based on the corrected evidences, we put forward a weighted assignment of conflicting mass where the weight index lies on the evidence credibility. As the normalization step is abolished, the conflict redistribution strategy avoids the conflicts caused by straightforward normalization. Through comprehensive conflict management, the proposed DS combination method can not only guarantee the rationality and availability of fusing results, but also enhance the reliability and robustness of a multisensor system. Finally, three combination experiments with different conflicting degrees illustrate the advantage and superiority of the novel combination method for conflict management. Consequently, the innovation of the novel algorithm is verified.

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Fuzzy Risk Evaluation in Failure Mode and Effects Analysis Using a D Numbers Based Multi-Sensor Information Fusion Method

Cited by 47 publications

References 62 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

A novel framework for the reliability modelling of repairable multistate complex mechanical systems considering propagation relationships

A Robust DS Combination Method Based on Evidence Correction and Conflict Redistribution

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