General model for the risk priority number in failure mode and effects analysis

Kim, Kyungmee O.; Zuo, Ming J.

doi:10.1016/j.ress.2017.09.010

Cited by 73 publications

(39 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the previous studies, it was observed that FMEA frameworks were modified to simplify the failure modes analysis and prioritization [43][44][45][46]. Similarly, the work of Nazeri and Naderikia [28] has clearly demonstrated an improved traditional FMEA by integrating expert weights, fuzzy methods, and other techniques into the analysis.…”

Section: Introductionmentioning

confidence: 98%

Identification and Prioritization of Risk Factors in an Electrical Generator Based on the Hybrid FMEA Framework

et al. 2019

View full text Add to dashboard Cite

The oil and gas industry is looking for ways to accurately identify and prioritize the failure modes (FMs) of the equipment. Failure mode and effect analysis (FMEA) is the most important tool used in the maintenance approach for the prevention of malfunctioning of the equipment. Current developments in the FMEA technique are mainly focused on addressing the drawbacks of the conventional risk priority number calculations, but the group effects and interrelationships of FMs on other measurements are neglected. In the present study, a hybrid distribution risk assessment framework was proposed to fill these gaps based on the combination of modified linguistic FMEA (LFMEA), Analytic Network Process (ANP), and Decision Making Trial and Evaluation Laboratory (DEMATEL) techniques. The hybrid framework of FMEA was conducted in a hazardous environment at a power generation unit in an oil and gas plant located in Yemen. The results show that mechanical and gas leakage FM in electrical generators posed a greater risk, which critically affects other FMs within the plant. It was observed that the suggested framework produced a precise ranking of FMs, with a clear relationship among FMs. Also, the comparisons of the proposed framework with previous studies demonstrated the multidisciplinary applications of the present framework.

show abstract

Section: Introductionmentioning

confidence: 98%

Identification and Prioritization of Risk Factors in an Electrical Generator Based on the Hybrid FMEA Framework

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The risk priority number is a product of the risk factor occurrence (O), severity (S), and detection (D). However, recent research has shown that these three factors are considered to be dependent (Kim & Zuo, ). In addition, most failure analyses have focused on the manufacturing field rather than the service sector, which has become increasingly important.…”

Section: Case Studymentioning

confidence: 99%

Multi‐criteria group decision‐making method based on TODIM with probabilistic interval‐valued hesitant fuzzy information

Zhang

Wang

2019

Expert Systems

View full text Add to dashboard Cite

Probabilistic interval‐valued hesitant fuzzy sets (PIV‐HFSs) are suitable for aggregating information from different groups because the probabilistic information of all the groups can be included by using interval values. Moreover, decision makers (DMs) prefer to use interval values to provide evaluation information. Furthermore, the traditional multi‐criteria group decision‐making (MCGDM) approach has some limitations, such as obtaining the DMs' weights with inappropriate methods and neglecting the interactions amongst the criteria and the psychological characteristics of DMs. Motivated by these research background, the main contents of this study are as follows. First, PIV‐HFSs are proposed, and the convex combination operation is extended into PIV‐HFSs. Second, a hybrid MCGDM approach with PIV‐HFSs is suggested that is based on the maximizing deviation method, fuzzy analytic network process (FANP) and TODIM (an acronym in Portuguese for interactive and multi‐criteria decision‐making model). Third, an evaluation case of health management centres based on the service‐specific failure mode and effect analysis (FMEA) is considered. The results show that the most crucial secondary factor is frequency (0.35775) and that the most serious failure mode is the inaccurate check‐in. The results demonstrate that the proposed model can evaluate service quality effectively and that it performs better than other methods.

show abstract

“…As a well‐accepted risk assessment tool in product, process, design, system, and service industries, traditional FMEA can analyse the risk of each failure and prevent hazardous accidents before reaching the customers. However, increasingly more researchers have criticized the conventional RPN, and the most important issues are as follows. In the calculation of the RPN, the risk factors, O , S , and D , are assigned the same weight for each failure.…”

Section: Improved Failure Mode and Effects Analysis Based On The D‐numentioning

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

View full text Add to dashboard Cite

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.

show abstract

General model for the risk priority number in failure mode and effects analysis

Cited by 73 publications

References 32 publications

Identification and Prioritization of Risk Factors in an Electrical Generator Based on the Hybrid FMEA Framework

Identification and Prioritization of Risk Factors in an Electrical Generator Based on the Hybrid FMEA Framework

Multi‐criteria group decision‐making method based on TODIM with probabilistic interval‐valued hesitant fuzzy information

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

Contact Info

Product

Resources

About