Modeling and Fusing the Uncertainty of FMEA Experts Using an Entropy-Like Measure with an Application in Fault Evaluation of Aircraft Turbine Rotor Blades

Zhou, Xuelian; Tang, Yongchuan

doi:10.3390/e20110864

Cited by 25 publications

(18 citation statements)

References 61 publications

(94 reference statements)

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“…In addition, uncertainty in expert opinions can be a cause to extend a robust approach to deal with the FMEA as an interesting further study. Finally, subjectivity and objectivity uncertainties would be integrated to get more realistic results.…”

Section: Resultsmentioning

confidence: 99%

Fuzzy smart failure modes and effects analysis to improve safety performance of system: Case study of an aircraft landing system

Daneshvar

Yazdi

Adesina³

2020

Quality & Reliability Eng

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Failure modes and effects analysis (FMEA) is a safety and reliability technique that is widely used to evaluate, design, and process a system against diverse possible ways through which the potential failure has a tendency to occur. In conventional FMEA, the risk evaluation is determined by risk priority number (RPN) obtained by multiplying of three risk factors—severity, occurrence, and detection. However, because of many shortages in conventional FMEA, the RPN scores have been widely criticized along issues bothering on ambiguity and vagueness, scoring, appraising, evaluating, and selecting corrective actions. In this paper, we propose a new integrated fuzzy smart FMEA framework where the combination of fuzzy set theory, analytical hierarchy process (AHP), and data envelopment analysis (DEA) is used, respectively, to handle uncertainty and to increase the reliability of the risk assessment. These are achieved by employing a heterogeneous group of experts and determining the efficiency of FMEA mode with adequate priority and corrective actions using RPN, time, and cost as indicators. A numerical example (aircraft landing system) is provided to exemplify the feasibility and effectiveness of the proposed model. The outputs of the proposed model compared with the conventional risk assessment technique results show its effectiveness, reliability, and propensity for real applications.

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Section: Resultsmentioning

confidence: 99%

Fuzzy smart failure modes and effects analysis to improve safety performance of system: Case study of an aircraft landing system

Daneshvar

Yazdi

Adesina³

2020

Quality & Reliability Eng

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“…In the study [13], a risk-based fuzzy evidential approach was put forward to use the interval-valued DST and fuzzy axiomatic design to assess the risk of failure modes. Similar studies also include [54,60].…”

Section: Dempster-shafer Eory (Dst)mentioning

confidence: 99%

The Risk Priority Number Evaluation of FMEA Analysis Based on Random Uncertainty and Fuzzy Uncertainty

2021

Complexity

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The risk priority number (RPN) calculation method is one of the critical subjects of failure mode and effects analysis (FMEA) research. Recently, RPN research under a fuzzy uncertainty environment has become a hot topic. Accordingly, increasing studies have ignored the important impact of the random sampling uncertainty in the FMEA assessment. In this study, a fuzzy beta-binomial RPN evaluation method is proposed by integrating fuzzy theory, Bayesian statistical inference, and the beta-binomial distribution. This model can effectively realize real-time, dynamic, and long-term evaluation of RPN under the condition of continuous knowledge accumulation. The major contribution of the proposed model is to use the random uncertainty and fuzzy uncertainty in an integrated model and provide a Markov Chain Monte Carlo (MCMC) method to solve the complex integrated model. The study presented a case study, which presented how to apply this model in practice and indicated the significant influence on the measurement error caused by ignoring the random uncertainty caused by expert evaluation in RPN calculations.

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“…In this step, rough set theory is also applied to manipulate the imprecision in the decision process. Then, y n ir is converted into rough number RI (y n ir ) following the equations (1)- (6).…”

Section: ) Obtain the Vague Intervals With Rough Set Theorymentioning

confidence: 99%

“…For example, the RPN does not consider the relatively weights of different risk factors, which is incorrect in practice. Because the relative importance of risk factors in different systems is usually different [6]. In RPN method, the evaluation scores are crisp which contain subjective and ambiguous information [7].…”

Section: Introductionmentioning

confidence: 99%

Risk Assessment in Intelligent Manufacturing Process: A Case Study of an Optical Cable Automatic Arranging Robot

Wan

et al. 2019

IEEE Access

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Intelligent manufacturing is one of the indispensable parts in Industry 4.0, where the smart machines can perceive and operate automatically in production process to provide higher and more convenient efficiency. Nevertheless, some potential failures influencing the manufacturing may exist. It is necessary to identify the potential failures and evaluate their risk. Meanwhile, resources and environment protections have been the consensus around the world. For enterprises, they must take measures to reduce the negative environmental impacts during the industrial production to maintain their sustainable development. Failure mode and effects analysis (FMEA) can effectively recognize failures and evaluate their risk in a production process or a system. However, environmental aspects of the identified failure modes are often ignored in the FMEA. Besides, conventional FMEA usually uses the risk priority number to obtain the failures' risk, which has a lot of shortcomings in the industrial application, such as the equal weights for different risk factors, crisp numbers used in the evaluation without considering vagueness, and so on. Based on the above two main problems, this paper develops an extended FMEA which introduces environmental impacts as one of the risk factors and utilizes Technique for order preference by similarity to an ideal solution (TOPSIS) method based on rough sets to cope with vague information. Finally, the developed FMEA is applied to evaluate the potential failure risk of an optical cable automatic arranging robot to verify its feasibility and effectiveness. INDEX TERMS Failure mode and effects analysis, intelligent manufacturing, environmental risk factor, subjective judgments.

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Modeling and Fusing the Uncertainty of FMEA Experts Using an Entropy-Like Measure with an Application in Fault Evaluation of Aircraft Turbine Rotor Blades

Cited by 25 publications

References 61 publications

Fuzzy smart failure modes and effects analysis to improve safety performance of system: Case study of an aircraft landing system

Fuzzy smart failure modes and effects analysis to improve safety performance of system: Case study of an aircraft landing system

The Risk Priority Number Evaluation of FMEA Analysis Based on Random Uncertainty and Fuzzy Uncertainty

Risk Assessment in Intelligent Manufacturing Process: A Case Study of an Optical Cable Automatic Arranging Robot

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