Failure mode and effects analysis using an improved pignistic probability transformation function and grey relational projection method
Abstract Failure mode and effects analysis (FMEA) is an important risk analysis tool that has been widely used in diverse areas to manage risk factors. However, how to manage the uncertainty in FMEA assessments is still an open issue. In this paper, a novel FMEA model based on the improved pignistic...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2023-11-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-023-01268-0 |
| _version_ | 1797233189992792064 |
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| author | Yongchuan Tang Zhaoxing Sun Deyun Zhou Yubo Huang |
| author_facet | Yongchuan Tang Zhaoxing Sun Deyun Zhou Yubo Huang |
| author_sort | Yongchuan Tang |
| collection | DOAJ |
| description | Abstract Failure mode and effects analysis (FMEA) is an important risk analysis tool that has been widely used in diverse areas to manage risk factors. However, how to manage the uncertainty in FMEA assessments is still an open issue. In this paper, a novel FMEA model based on the improved pignistic probability transformation function in Dempster–Shafer evidence theory (DST) and grey relational projection method (GRPM) is proposed to improve the accuracy and reliability in risk analysis with FMEA. The basic probability assignment (BPA) function in DST is used to model the assessments of experts with respect to each risk factor. Dempster’s rule of combination is adopted for fusion of assessment information from different experts. The improved pignistic probability function is proposed and used to transform the fusion result of BPA into probability function for getting more accurate decision-making result in risk analysis with FMEA. GRPM is adopted to determine the risk priority order of all the failure modes to overcome the shortcoming in traditional risk priority number in FMEA. Applications in aircraft turbine rotor blades and steel production process are presented to show the rationality and generality of the proposed method. |
| first_indexed | 2024-04-24T16:12:13Z |
| format | Article |
| id | doaj.art-82e3b424b3ab4675a08804c5595e8a39 |
| institution | Directory Open Access Journal |
| issn | 2199-4536 2198-6053 |
| language | English |
| last_indexed | 2024-04-24T16:12:13Z |
| publishDate | 2023-11-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj.art-82e3b424b3ab4675a08804c5595e8a392024-03-31T11:39:21ZengSpringerComplex & Intelligent Systems2199-45362198-60532023-11-011022233224710.1007/s40747-023-01268-0Failure mode and effects analysis using an improved pignistic probability transformation function and grey relational projection methodYongchuan Tang0Zhaoxing Sun1Deyun Zhou2Yubo Huang3School of Microelectronics, Northwestern Polytechnical UniversitySchool of Microelectronics, Northwestern Polytechnical UniversitySchool of Microelectronics, Northwestern Polytechnical UniversityIntelligent Control and Smart Energy (ICSE) Research Group, School of Engineering, University of WarwickAbstract Failure mode and effects analysis (FMEA) is an important risk analysis tool that has been widely used in diverse areas to manage risk factors. However, how to manage the uncertainty in FMEA assessments is still an open issue. In this paper, a novel FMEA model based on the improved pignistic probability transformation function in Dempster–Shafer evidence theory (DST) and grey relational projection method (GRPM) is proposed to improve the accuracy and reliability in risk analysis with FMEA. The basic probability assignment (BPA) function in DST is used to model the assessments of experts with respect to each risk factor. Dempster’s rule of combination is adopted for fusion of assessment information from different experts. The improved pignistic probability function is proposed and used to transform the fusion result of BPA into probability function for getting more accurate decision-making result in risk analysis with FMEA. GRPM is adopted to determine the risk priority order of all the failure modes to overcome the shortcoming in traditional risk priority number in FMEA. Applications in aircraft turbine rotor blades and steel production process are presented to show the rationality and generality of the proposed method.https://doi.org/10.1007/s40747-023-01268-0Failure mode and effects analysisDempster–Shafer evidence theoryPignistic probability transformation functionGrey relational projection methodRisk analysis |
| spellingShingle | Yongchuan Tang Zhaoxing Sun Deyun Zhou Yubo Huang Failure mode and effects analysis using an improved pignistic probability transformation function and grey relational projection method Complex & Intelligent Systems Failure mode and effects analysis Dempster–Shafer evidence theory Pignistic probability transformation function Grey relational projection method Risk analysis |
| title | Failure mode and effects analysis using an improved pignistic probability transformation function and grey relational projection method |
| title_full | Failure mode and effects analysis using an improved pignistic probability transformation function and grey relational projection method |
| title_fullStr | Failure mode and effects analysis using an improved pignistic probability transformation function and grey relational projection method |
| title_full_unstemmed | Failure mode and effects analysis using an improved pignistic probability transformation function and grey relational projection method |
| title_short | Failure mode and effects analysis using an improved pignistic probability transformation function and grey relational projection method |
| title_sort | failure mode and effects analysis using an improved pignistic probability transformation function and grey relational projection method |
| topic | Failure mode and effects analysis Dempster–Shafer evidence theory Pignistic probability transformation function Grey relational projection method Risk analysis |
| url | https://doi.org/10.1007/s40747-023-01268-0 |
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