Modified failure mode and effects analysis under uncertainty: A rough cloud theory-based approach

Li, Jing; Fang, Hongyuan; Song, Wenyan

doi:10.1016/j.asoc.2019.02.029

Cited by 80 publications

(25 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Peng and Wang [21] presented a linguistic intuitionistic cloud model. Li et al [22] combined rough sets and cloud models and applied a rough cloud theory-based method to risk evaluation of failure modes.…”

Section: Madm Techniques Are Common Methods To Construct Informationmentioning

confidence: 99%

An Extension of Regret Theory Based on Probabilistic Linguistic Cloud Sets Considering Dual Expectations: An Application for the Stock Market

Gong

2019

IEEE Access

View full text Add to dashboard Cite

The decision of stock selection relies on experts' cognition and investors' behavioral characteristics. This requires the consideration of several conflicting, often fuzzy criteria with uncertainty conditions, which can benefit from multiple attribute decision-making (MADM). Accordingly, an extended regret theory (RT) decision-making method is developed in this study to identify and rank-order superior stocks. First, by extracting the strengths of probabilistic linguistic term sets and cloud models, a novel concept of probabilistic linguistic cloud sets (PLCSs) is proposed to effectively express and handle uncertain preference information. Second, RT is extended to the PLCSs environment. Considering the behavior characteristics of expectation dependence, dual (target and growth) expectations are shown. Third, a distance measure algorithm of PLCSs is defined to calculate the distance between the attribute value and dual expectations, which serves as the basis for the construction of a fuzzy pattern recognition model to determine the optimal membership and attribute weights. Membership is used to modify the RT-based perceived utility, the ranking of alternatives is determined by the modified comprehensive perceived utility. A case study is conducted to demonstrate the proposed method, and its reliability and effectiveness are further verified by comparing it with other methods.

show abstract

Section: Madm Techniques Are Common Methods To Construct Informationmentioning

confidence: 99%

An Extension of Regret Theory Based on Probabilistic Linguistic Cloud Sets Considering Dual Expectations: An Application for the Stock Market

Gong

2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…e studies [57,58] integrated the rough set theory and the TOPSIS to evaluate the risk of failure modes. Another study [59] integrates the rough set theory and the cloud model theory in FMEA analysis.…”

Section: Rough Set Eorymentioning

confidence: 99%

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

2021

Complexity

View full text Add to dashboard Cite

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.

show abstract

“…Liu et al [44] performed risk analysis of scraper arm control system and Lei et al [45] performed risk analysis using of metro vehicle by integrating cloud model and TOPSIS approach. Failure causes of a steam valve system is identified using Cloud model and extended TOPSIS [46]. Hu et al [47] performed risk analysis of health care department by ranking the failure causes using cloud model and GRA-TOPSIS approach.…”

Section: Literature Reviewmentioning

confidence: 99%

Critical ranking of steam handling unit using integrated cloud model and extended PROMETHEE for maintenance purpose

Panwar

Kumar

2020

Complex Intell. Syst.

View full text Add to dashboard Cite

To determine the critical component in an industry is one of the most important tasks performed by maintenance personnel to choose the best maintenance policy. Therefore, the purpose of the current paper is to develop a methodology based on integrated cloud model and extended preference ranking organization method for enrichment evaluation (PROMETHEE) method for finding the most critical component of the framework by ranking the failure causes of the system from multiple decision maker perspective. For this purpose, ranking of failure causes is performed by taking into account five factors namely chances of occurrence of failure (F0), non-detection probability (Nd), downtime duration (Dd), spare part criticality (Spc) and safety risk (Sr). In this paper, first the primary and secondary weight of decision makers are calculated based on the uncertainty degree and divergence degree, respectively, to determine overall weight using cloud model theory by converting the uncertain linguistic evaluation matrix into interval cloud matrix, and then ranking of the steam handling subunit of paper making unit in a paper mill using extended PROMETHEE. The effectiveness of the proposed methodology is explained by considering steam handling subunit of paper making unit to find the critical component.

show abstract

Modified failure mode and effects analysis under uncertainty: A rough cloud theory-based approach

Cited by 80 publications

References 39 publications

An Extension of Regret Theory Based on Probabilistic Linguistic Cloud Sets Considering Dual Expectations: An Application for the Stock Market

An Extension of Regret Theory Based on Probabilistic Linguistic Cloud Sets Considering Dual Expectations: An Application for the Stock Market

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

Critical ranking of steam handling unit using integrated cloud model and extended PROMETHEE for maintenance purpose

Contact Info

Product

Resources

About