Risk assessment by failure mode and effects analysis (FMEA) using an interval number based logistic regression model
•This paper addresses the shortcomings of traditional RPN calculation during failure mode and effect analysis, and proposes a systematic approach for identifying and evaluating potential failures using the methodology of interval number based logistic regression approach. A comparative analysis of t...
Saved in:
| Published in: | Safety science Vol. 132; p. 104967 |
|---|---|
| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Amsterdam
Elsevier Ltd
01.12.2020
Elsevier BV |
| Subjects: | |
| ISSN: | 0925-7535, 1879-1042 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | •This paper addresses the shortcomings of traditional RPN calculation during failure mode and effect analysis, and proposes a systematic approach for identifying and evaluating potential failures using the methodology of interval number based logistic regression approach. A comparative analysis of the existing approaches and proposed methodology is also provided.•Logistic regression allows comparing the effects of variables measured on different scales and it indicates the significant relationships between dependent variable and independent variable. Thus, it helps to associate coefficients with the risk factors of failure. The coefficient in the logistic regression equation helps to know the degree of importance of each risk factor in the failure.•In failure analysis, it is used to predict the likelihood of failure, and investigating the importance of related factors contributing to failure. With the help of data of submersible pumps as a case study, an equation of probability of risk of failure 'P' is found through R software. Thus, a novel attempt has been made for the first time to investigate the limitations of traditional RPN calculation statistically.
In order to reduce risks of failure, industries use a methodology called Failure Mode and Effects Analysis (FMEA) in terms of the Risk Priority Number (RPN). The RPN number is a product of ordinal scale variables, severity (S), occurrence (O) and detection (D) and product of such ordinal variables is debatable. The three risk attributes (S, O, and D) are generally given equal weightage, but this assumption may not be suitable for real-world applications. Apart from severity, occurrence, and detection, the presence of other risk attributes may also influence the risk of failure and hence should be considered for achieving a holistic approach towards mitigating failure modes. This paper proposes a systematic approach for developing a standard equation for RPN measure, using the methodology of interval number based logistic regression. Instead of utilizing RPN in product form for each failure, this method is benefited from decisions based on probability of risk of failure, 'P' which is more realistic in practical applications. A case study is presented to illustrate the application of the proposed methodology in finding the risk of failure of high capacity submersible pumps in the power plant. The developed logistic regression model (logit model) using R software helped in generating the probability of risk of failure equation for predicting the failures. The model showed the correct classification rate to be 77.47%. The Receiver Operating Characteristic (ROC) curve showed the logit-model to be 81.98% accurate with an optimal cut-off value of 0.56. |
|---|---|
| AbstractList | In order to reduce risks of failure, industries use a methodology called Failure Mode and Effects Analysis (FMEA) in terms of the Risk Priority Number (RPN). The RPN number is a product of ordinal scale variables, severity (S), occurrence (O) and detection (D) and product of such ordinal variables is debatable. The three risk attributes (S, O, and D) are generally given equal weightage, but this assumption may not be suitable for real-world applications. Apart from severity, occurrence, and detection, the presence of other risk attributes may also influence the risk of failure and hence should be considered for achieving a holistic approach towards mitigating failure modes. This paper proposes a systematic approach for developing a standard equation for RPN measure, using the methodology of interval number based logistic regression. Instead of utilizing RPN in product form for each failure, this method is benefited from decisions based on probability of risk of failure, 'P" which is more realistic in practical applications. A case study is presented to illustrate the application of the proposed methodology in finding the risk of failure of high capacity submersible pumps in the power plant. The developed logistic regression model (logit model) using R software helped in generating the probability of risk of failure equation for predicting the failures. The model showed the correct classification rate to be 77.47%. The Receiver Operating Characteristic (ROC) curve showed the logit-model to be 81.98% accurate with an optimal cut-off value of 0.56. •This paper addresses the shortcomings of traditional RPN calculation during failure mode and effect analysis, and proposes a systematic approach for identifying and evaluating potential failures using the methodology of interval number based logistic regression approach. A comparative analysis of the existing approaches and proposed methodology is also provided.•Logistic regression allows comparing the effects of variables measured on different scales and it indicates the significant relationships between dependent variable and independent variable. Thus, it helps to associate coefficients with the risk factors of failure. The coefficient in the logistic regression equation helps to know the degree of importance of each risk factor in the failure.•In failure analysis, it is used to predict the likelihood of failure, and investigating the importance of related factors contributing to failure. With the help of data of submersible pumps as a case study, an equation of probability of risk of failure 'P' is found through R software. Thus, a novel attempt has been made for the first time to investigate the limitations of traditional RPN calculation statistically. In order to reduce risks of failure, industries use a methodology called Failure Mode and Effects Analysis (FMEA) in terms of the Risk Priority Number (RPN). The RPN number is a product of ordinal scale variables, severity (S), occurrence (O) and detection (D) and product of such ordinal variables is debatable. The three risk attributes (S, O, and D) are generally given equal weightage, but this assumption may not be suitable for real-world applications. Apart from severity, occurrence, and detection, the presence of other risk attributes may also influence the risk of failure and hence should be considered for achieving a holistic approach towards mitigating failure modes. This paper proposes a systematic approach for developing a standard equation for RPN measure, using the methodology of interval number based logistic regression. Instead of utilizing RPN in product form for each failure, this method is benefited from decisions based on probability of risk of failure, 'P' which is more realistic in practical applications. A case study is presented to illustrate the application of the proposed methodology in finding the risk of failure of high capacity submersible pumps in the power plant. The developed logistic regression model (logit model) using R software helped in generating the probability of risk of failure equation for predicting the failures. The model showed the correct classification rate to be 77.47%. The Receiver Operating Characteristic (ROC) curve showed the logit-model to be 81.98% accurate with an optimal cut-off value of 0.56. |
| ArticleNumber | 104967 |
| Author | Bhattacharjee, Pushparenu Dey, Vidyut Mandal, U.K. |
| Author_xml | – sequence: 1 givenname: Pushparenu surname: Bhattacharjee fullname: Bhattacharjee, Pushparenu email: pshpbhattacharjee@gmail.com, pshpb.pe@nita.ac.in – sequence: 2 givenname: Vidyut surname: Dey fullname: Dey, Vidyut – sequence: 3 givenname: U.K. surname: Mandal fullname: Mandal, U.K. |
| BookMark | eNp9kM1qGzEURkVJoU7SF-hK0E27GFc_lkaCbkJI2kJKoCRrodFcGbljTaqrCfjtI9dddZGVpMt3PnTPOTnLcwZCPnC25ozrL7s1YkhrwcRxsLG6f0NW3PS2ay9xRlbMCtX1Sqp35BxxxxjjUvMVqb8S_qYeERD3kCsdDjT6NC0F6H4egfo8UogRQsV299MBE9JPtz9vrj7TBVPetilNuUJ59hPNy36AQgePMNJp3iasKdAC29L605z_dk6X5G30E8L7f-cFeby9ebj-3t3df_txfXXXBSlM7VS0UeshMKmNtNqwCFENBvToLYhBGcFZ7MMQbS823MBoQXrGlZW9YSaAvCAfT71PZf6zAFa3m5fSlkAnNkYrJSTXLWVOqVBmxALRhVR9bb-tpZlwnLmjY7dzR8fu6NidHDdU_Ic-lbT35fA69PUEQVv9OUFxLQE5wJhK0-zGOb2GvwDnGpha |
| CitedBy_id | crossref_primary_10_1016_j_oceaneng_2022_111187 crossref_primary_10_3390_sym17020151 crossref_primary_10_1016_j_oceaneng_2023_113888 crossref_primary_10_1016_j_oceaneng_2023_114217 crossref_primary_10_1016_j_ast_2022_107730 crossref_primary_10_1016_j_psep_2021_12_025 crossref_primary_10_3390_jrfm14040187 crossref_primary_10_1002_ppp_2135 crossref_primary_10_1007_s42452_021_04805_z crossref_primary_10_1002_qre_3416 crossref_primary_10_1007_s00769_025_01661_x crossref_primary_10_3390_math12111660 crossref_primary_10_3390_s24113511 crossref_primary_10_3390_app12126144 crossref_primary_10_1007_s00500_021_05605_8 crossref_primary_10_1016_j_psep_2025_106919 crossref_primary_10_3390_ijerph17197054 crossref_primary_10_1016_j_cie_2023_109758 crossref_primary_10_1155_2024_4369401 crossref_primary_10_3390_math12182842 crossref_primary_10_1016_j_engappai_2024_108655 crossref_primary_10_1016_j_aei_2022_101712 crossref_primary_10_1007_s00500_022_07264_9 crossref_primary_10_1002_ese3_1223 crossref_primary_10_1016_j_engfailanal_2023_107675 crossref_primary_10_1007_s40815_022_01302_2 crossref_primary_10_1016_j_engfailanal_2021_105775 crossref_primary_10_3390_en15239003 crossref_primary_10_1016_j_istruc_2022_09_110 crossref_primary_10_1016_j_ress_2022_108703 crossref_primary_10_3390_pr10101973 crossref_primary_10_1016_j_ssci_2025_106933 crossref_primary_10_3390_en15176263 crossref_primary_10_1061__ASCE_CO_1943_7862_0002354 crossref_primary_10_3390_e25050757 crossref_primary_10_1002_qre_3751 crossref_primary_10_3233_JIFS_222652 crossref_primary_10_23919_JSEE_2024_000124 crossref_primary_10_1108_IJQRM_01_2025_0042 crossref_primary_10_1016_j_jairtraman_2025_102902 crossref_primary_10_1109_TEM_2024_3395649 crossref_primary_10_1115_1_4069023 crossref_primary_10_3390_e22101162 crossref_primary_10_1016_j_trd_2024_104404 crossref_primary_10_1155_2021_6666126 crossref_primary_10_1016_j_ress_2022_108333 crossref_primary_10_1016_j_ces_2022_117502 crossref_primary_10_1016_j_cie_2023_109031 crossref_primary_10_1007_s40815_022_01412_x crossref_primary_10_1007_s41683_021_00075_4 crossref_primary_10_1108_DTA_06_2022_0232 crossref_primary_10_3390_app11167349 crossref_primary_10_3390_wevj15080365 crossref_primary_10_1016_j_ress_2024_110308 crossref_primary_10_1093_labmed_lmae092 crossref_primary_10_1016_j_ast_2024_109688 crossref_primary_10_3390_su141912357 crossref_primary_10_1108_BIJ_03_2024_0212 crossref_primary_10_1155_2021_9125605 crossref_primary_10_1002_qre_2870 crossref_primary_10_1007_s13369_025_10000_8 crossref_primary_10_1007_s40999_025_01104_1 crossref_primary_10_1007_s13198_023_01981_6 crossref_primary_10_1016_j_cie_2021_107439 crossref_primary_10_1016_j_oceaneng_2025_121998 crossref_primary_10_3390_math12121843 crossref_primary_10_1007_s00202_023_01776_9 crossref_primary_10_1007_s11668_025_02199_3 crossref_primary_10_1016_j_eswa_2021_115030 crossref_primary_10_1016_j_eswa_2022_118798 crossref_primary_10_1177_0734242X211003133 crossref_primary_10_1007_s12010_021_03720_8 crossref_primary_10_1109_ACCESS_2022_3172513 crossref_primary_10_1016_j_psep_2022_12_006 crossref_primary_10_1016_j_psep_2024_04_025 crossref_primary_10_3390_axioms10020104 crossref_primary_10_1016_j_jclepro_2024_143927 crossref_primary_10_1016_j_ecolind_2023_111137 |
| Cites_doi | 10.1108/02656710110383737 10.1007/s12008-018-0496-2 10.3390/math7100874 10.1108/02656710510625248 10.21037/atm.2016.02.15 10.1016/j.jlp.2006.10.003 10.1016/j.jlp.2010.05.003 10.1080/03088839.2018.1520401 10.1016/j.surg.2015.12.029 10.1016/j.cie.2019.07.051 10.1016/j.oceaneng.2019.106214 10.1002/hfm.20729 10.1016/j.eswa.2012.08.010 10.1016/j.cor.2008.05.002 10.1016/j.knosys.2018.05.030 10.1002/qre.1633 10.1016/j.ress.2017.09.017 10.5334/irsp.90 10.1080/00207540110056162 10.1016/j.pnucene.2005.03.016 10.1002/qre.1791 10.1080/0951192X.2013.785027 10.1016/j.ssci.2017.10.018 10.1108/02656719310040105 10.1080/23311916.2017.1284373 10.3846/16484142.2016.1133454 10.1016/j.ssci.2017.06.009 10.1016/S0019-9958(65)90241-X 10.1109/TR.2013.2241251 10.1016/0951-8320(95)00068-D 10.1016/j.tre.2019.03.011 10.1001/jama.2016.20441 10.1093/ije/dyu029 10.2214/ajr.184.2.01840364 10.1214/08-AOAS191 10.1016/j.jclepro.2018.04.167 10.1002/9781118312575 10.1016/j.asoc.2018.09.020 10.1016/j.patrec.2005.10.012 10.1016/0020-0255(75)90017-1 |
| ContentType | Journal Article |
| Copyright | 2020 Elsevier Ltd Copyright Elsevier BV Dec 2020 |
| Copyright_xml | – notice: 2020 Elsevier Ltd – notice: Copyright Elsevier BV Dec 2020 |
| DBID | AAYXX CITATION 7QF 7QQ 7SC 7SE 7SP 7SR 7T2 7TA 7TB 7U5 8BQ 8FD C1K F28 FR3 H8D H8G JG9 JQ2 KR7 L7M L~C L~D NAPCQ |
| DOI | 10.1016/j.ssci.2020.104967 |
| DatabaseName | CrossRef Aluminium Industry Abstracts Ceramic Abstracts Computer and Information Systems Abstracts Corrosion Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Health and Safety Science Abstracts (Full archive) Materials Business File Mechanical & Transportation Engineering Abstracts Solid State and Superconductivity Abstracts METADEX Technology Research Database Environmental Sciences and Pollution Management ANTE: Abstracts in New Technology & Engineering Engineering Research Database Aerospace Database Copper Technical Reference Library Materials Research Database ProQuest Computer Science Collection Civil Engineering Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Nursing & Allied Health Premium |
| DatabaseTitle | CrossRef Materials Research Database Civil Engineering Abstracts Aluminium Industry Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Ceramic Abstracts Materials Business File METADEX Environmental Sciences and Pollution Management Computer and Information Systems Abstracts Professional Aerospace Database Copper Technical Reference Library Nursing & Allied Health Premium Engineered Materials Abstracts Health & Safety Science Abstracts Solid State and Superconductivity Abstracts Engineering Research Database Corrosion Abstracts Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering |
| DatabaseTitleList | Materials Research Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Economics Public Health |
| EISSN | 1879-1042 |
| ExternalDocumentID | 10_1016_j_ssci_2020_104967 S0925753520303647 |
| GroupedDBID | --- --K --M .~1 0R~ 123 13V 1B1 1RT 1~. 1~5 29P 4.4 457 4G. 53G 5VS 7-5 71M 8P~ 9JM 9JN 9JO AABNK AACTN AAEDT AAEDW AAFJI AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO ABBQC ABFNM ABIVO ABJNI ABKBG ABLVK ABMAC ABMMH ABMVD ABMZM ABNUV ABXDB ABYKQ ACDAQ ACGFS ACHRH ACIWK ACJTP ACNNM ACNTT ACPRK ACRLP ADBBV ADEWK ADEZE ADMUD ADTZH AEBSH AECPX AEKER AENEX AFKWA AFRAH AFTJW AFXBA AFXIZ AGHFR AGJBL AGUBO AGUMN AGYEJ AHHHB AHJVU AHPOS AIEXJ AIKHN AISVY AITUG AJBFU AJOXV AJRQY AKURH AKYCK ALEQD ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ ANZVX AOMHK ASPBG AVARZ AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC BNPGV BNSAS CS3 DU5 EBS EFJIC EFLBG EJD ENUVR EO8 EO9 EP2 EP3 F3I F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA HEH HMK HMO HMY HVGLF HZ~ IHE J1W JJJVA KOM LCYCR M29 M3W M3Y M41 MO0 N9A NAHTW O-L O9- OAUVE OZT P-8 P-9 P2P PC. PQQKQ PRBVW Q38 R2- RIG ROL RPZ SAE SDF SDG SES SEW SNG SPC SPCBC SSB SSG SSH SSL SSO SSS SST SSZ T5K UHS WH7 WUQ YHZ ~02 ~G- 9DU AATTM AAXKI AAYWO AAYXX ABWVN ACIEU ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD 7QF 7QQ 7SC 7SE 7SP 7SR 7T2 7TA 7TB 7U5 8BQ 8FD AGCQF C1K F28 FR3 H8D H8G JG9 JQ2 KR7 L7M L~C L~D NAPCQ |
| ID | FETCH-LOGICAL-c328t-5f9f66bc036839680fef5b8e6da9e2b58210f7cbf972418ed9e3a015937808ce3 |
| ISICitedReferencesCount | 91 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000582127200017&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0925-7535 |
| IngestDate | Wed Aug 13 09:41:27 EDT 2025 Tue Nov 18 21:26:03 EST 2025 Sat Nov 29 07:09:42 EST 2025 Fri Feb 23 02:46:43 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Logistic regression Probability of risk of failure FMEA Risk assessment Interval number Machine learning Risk Priority Number (RPN) |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c328t-5f9f66bc036839680fef5b8e6da9e2b58210f7cbf972418ed9e3a015937808ce3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| PQID | 2486552316 |
| PQPubID | 2045403 |
| ParticipantIDs | proquest_journals_2486552316 crossref_citationtrail_10_1016_j_ssci_2020_104967 crossref_primary_10_1016_j_ssci_2020_104967 elsevier_sciencedirect_doi_10_1016_j_ssci_2020_104967 |
| PublicationCentury | 2000 |
| PublicationDate | December 2020 2020-12-00 20201201 |
| PublicationDateYYYYMMDD | 2020-12-01 |
| PublicationDate_xml | – month: 12 year: 2020 text: December 2020 |
| PublicationDecade | 2020 |
| PublicationPlace | Amsterdam |
| PublicationPlace_xml | – name: Amsterdam |
| PublicationTitle | Safety science |
| PublicationYear | 2020 |
| Publisher | Elsevier Ltd Elsevier BV |
| Publisher_xml | – name: Elsevier Ltd – name: Elsevier BV |
| References | Vienna, Austria: R foundation for statistical computing. Garcia, Schirru (b0075) 2005; 46 Wan, Yan, Zhang, Qu, Yang (b0235) 2019; 125 Prem, Ng, Mannan (b0190) 2010; 23 Efe (b0055) 2019; 187 Obuchowski (b0175) 2005; 184 Fattahi, Khalilzadeh (b0060) 2018; 102 Kleinbaum, Dietz, Gail, Klein, Klein (b0115) 2002 Nie, Tian, Wang, Wang, Wang (b0170) 2018; 162 Stamatis (b0225) 2003 Zadeh (b0260) 1975; 9 Yazdi, Daneshvar, Setareh (b0250) 2017; 98 Franceschini, Galetto (b0070) 2001; 39 Kerns, G.J., 2010. Yazdi (b0245) 2019; 13 Song, Ming, Wu, Zhu (b0220) 2013; 26 Zhang, Z. (2016). Model building strategy for logistic regression: purposeful selection. Ann. Translat. Med. 4(6), 111–111. Bowles, Peláez (b0015) 1995; 50 Liu, Liu, Lin (b0130) 2013; 62 Carter, Pan, Rai, Galandiuk (b0035) 2016; 159 Arabsheybani, Paydar, Safaei (b0010) 2018; 190 Can (b0020) 2018; 28 Gilchrist (b0090) 1993 Wang, Liu, Chen, Qin (b0240) 2019; 136 Rouhparvar, Mazandarani Zadeh, Nasirzadeh (b0195) 2014; 25 Muller, MacLehose (b0160) 2014; 43 Sankar, Prabhu (b0200) 2001; 18 Fawcett (b0065) 2006; 27 Akyuz, Celik (b0005) 2018; 45 Carpitella, Certa, Izquierdo, La Fata (b0030) 2018; 169 Meel, A., O’neill, L. M., Levin, J. H., Seider, W. D., Oktem, U., Keren, N., 2007. Operational risk assessment of chemical industries by exploiting accident databases. J. Loss Prevent. Process Industr. 20(2), 113–127. Zadeh (b0255) 1965; 8 Carlson, C., 2012. Effective FMEAs: Achieving safe, reliable, and economical products and processes using failure mode and effects analysis Chin, Wang, Poon, Yang (b0045) 2009; 36 Paul, Jana, Mondal, Bhattacharya (b0180) 2017; 33 Team, R. C. (2000). R language definition. Liu, Liu, Liu (b0135) 2013; 40 Hosmer, Lemeshow (b0100) 2000 Lo, Liou (b0140) 2018; 73 Meurer, Tolles (b0155) 2017; 317 Ebeling (b0050) 2004 (Vol. 1). John Wiley & Sons. Liu, Li, You, Chen (b0125) 2015; 31 Hajiagha, Hashemi, Mohammadi, Zavadskas (b0095) 2016; 31 Selim, Yunusoglu, Yılmaz Balaman (b0205) 2016; 32 Manning, C. (2007). Logistic regression (with R). Changes. Karimi, Pasandideh (b0105) 2018; 11 Chang, Lo, Chen, Liou (b0040) 2019; 7 Introduction to probability and statistics using r. Lulu. com. Gareth, Witten, Hastie, Tibshirani (b0080) 2013 Li, Li, Chen, He, Hou, Chen (b0120) 2019 Narkhede (b0165) 2018; 26 Sommet, Morselli (b0215) 2017; 30 Gelman, Jakulin, Pittau, Su (b0085) 2008; 2 Sharma, Kumar, Kumar (b0210) 2005; 22 Pazireh, Sadeghi, Shokohyar (b0185) 2017; 4 Chin (10.1016/j.ssci.2020.104967_b0045) 2009; 36 Li (10.1016/j.ssci.2020.104967_b0120) 2019 Meurer (10.1016/j.ssci.2020.104967_b0155) 2017; 317 Hosmer (10.1016/j.ssci.2020.104967_b0100) 2000 10.1016/j.ssci.2020.104967_b0025 10.1016/j.ssci.2020.104967_b0145 10.1016/j.ssci.2020.104967_b0265 Fattahi (10.1016/j.ssci.2020.104967_b0060) 2018; 102 Muller (10.1016/j.ssci.2020.104967_b0160) 2014; 43 Zadeh (10.1016/j.ssci.2020.104967_b0260) 1975; 9 Paul (10.1016/j.ssci.2020.104967_b0180) 2017; 33 Efe (10.1016/j.ssci.2020.104967_b0055) 2019; 187 Garcia (10.1016/j.ssci.2020.104967_b0075) 2005; 46 Bowles (10.1016/j.ssci.2020.104967_b0015) 1995; 50 Wang (10.1016/j.ssci.2020.104967_b0240) 2019; 136 Akyuz (10.1016/j.ssci.2020.104967_b0005) 2018; 45 Franceschini (10.1016/j.ssci.2020.104967_b0070) 2001; 39 Stamatis (10.1016/j.ssci.2020.104967_b0225) 2003 Sharma (10.1016/j.ssci.2020.104967_b0210) 2005; 22 Yazdi (10.1016/j.ssci.2020.104967_b0250) 2017; 98 Kleinbaum (10.1016/j.ssci.2020.104967_b0115) 2002 Sankar (10.1016/j.ssci.2020.104967_b0200) 2001; 18 Obuchowski (10.1016/j.ssci.2020.104967_b0175) 2005; 184 Wan (10.1016/j.ssci.2020.104967_b0235) 2019; 125 Fawcett (10.1016/j.ssci.2020.104967_b0065) 2006; 27 Song (10.1016/j.ssci.2020.104967_b0220) 2013; 26 Hajiagha (10.1016/j.ssci.2020.104967_b0095) 2016; 31 Zadeh (10.1016/j.ssci.2020.104967_b0255) 1965; 8 Liu (10.1016/j.ssci.2020.104967_b0135) 2013; 40 Arabsheybani (10.1016/j.ssci.2020.104967_b0010) 2018; 190 Narkhede (10.1016/j.ssci.2020.104967_b0165) 2018; 26 Liu (10.1016/j.ssci.2020.104967_b0125) 2015; 31 Nie (10.1016/j.ssci.2020.104967_b0170) 2018; 162 Yazdi (10.1016/j.ssci.2020.104967_b0245) 2019; 13 Gelman (10.1016/j.ssci.2020.104967_b0085) 2008; 2 Prem (10.1016/j.ssci.2020.104967_b0190) 2010; 23 Carter (10.1016/j.ssci.2020.104967_b0035) 2016; 159 Can (10.1016/j.ssci.2020.104967_b0020) 2018; 28 Gareth (10.1016/j.ssci.2020.104967_b0080) 2013 Karimi (10.1016/j.ssci.2020.104967_b0105) 2018; 11 Carpitella (10.1016/j.ssci.2020.104967_b0030) 2018; 169 Gilchrist (10.1016/j.ssci.2020.104967_b0090) 1993 10.1016/j.ssci.2020.104967_b0110 Liu (10.1016/j.ssci.2020.104967_b0130) 2013; 62 10.1016/j.ssci.2020.104967_b0230 Pazireh (10.1016/j.ssci.2020.104967_b0185) 2017; 4 Sommet (10.1016/j.ssci.2020.104967_b0215) 2017; 30 Rouhparvar (10.1016/j.ssci.2020.104967_b0195) 2014; 25 10.1016/j.ssci.2020.104967_b0150 Lo (10.1016/j.ssci.2020.104967_b0140) 2018; 73 Ebeling (10.1016/j.ssci.2020.104967_b0050) 2004 Selim (10.1016/j.ssci.2020.104967_b0205) 2016; 32 Chang (10.1016/j.ssci.2020.104967_b0040) 2019; 7 |
| References_xml | – volume: 25 start-page: 83 year: 2014 end-page: 94 ident: b0195 article-title: Quantitative risk allocation in construction projects: a fuzzy-bargaining game approach publication-title: Int. J. Indust. Eng. Product. Res. – volume: 28 start-page: 130 year: 2018 end-page: 147 ident: b0020 article-title: An intuitionistic approach based on failure mode and effect analysis for prioritizing corrective and preventive strategies publication-title: Hum. Factors Ergon. Manuf. Serv. Ind. – volume: 31 start-page: 761 year: 2015 end-page: 772 ident: b0125 article-title: A novel approach for FMEA: Combination of interval 2-tuple linguistic variables and grey relational analysis publication-title: Qual. Reliab. Eng. Int. – year: 2000 ident: b0100 article-title: Logistic Regression – reference: Carlson, C., 2012. Effective FMEAs: Achieving safe, reliable, and economical products and processes using failure mode and effects analysis – volume: 50 start-page: 203 year: 1995 end-page: 213 ident: b0015 article-title: Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis publication-title: Reliab. Eng. Syst. Saf. – volume: 9 start-page: 43 year: 1975 end-page: 80 ident: b0260 article-title: The concept of a linguistic variable and its application to approximate reasoning-III publication-title: Inf. Sci. – volume: 73 start-page: 684 year: 2018 end-page: 696 ident: b0140 article-title: A novel multiple-criteria decision-making-based FMEA model for risk assessment publication-title: Appl. Soft Comput. – year: 2013 ident: b0080 article-title: An introduction to statistical learning – reference: Kerns, G.J., 2010. – volume: 184 start-page: 364 year: 2005 end-page: 372 ident: b0175 article-title: ROC analysis publication-title: Am. J. Roentgenol. – volume: 98 start-page: 113 year: 2017 end-page: 123 ident: b0250 article-title: An extension to fuzzy developed failure mode and effects analysis (FDFMEA) application for aircraft landing system publication-title: Saf. Sci. – volume: 13 start-page: 441 year: 2019 end-page: 458 ident: b0245 article-title: Improving failure mode and effect analysis (FMEA) with consideration of uncertainty handling as an interactive approach publication-title: Int. J. Int. Des. Manufact. (IJIDeM) – reference: Manning, C. (2007). Logistic regression (with R). Changes. – volume: 317 start-page: 1068 year: 2017 end-page: 1069 ident: b0155 article-title: Logistic regression diagnostics: understanding how well a model predicts outcomes publication-title: JAMA – volume: 169 start-page: 394 year: 2018 end-page: 402 ident: b0030 article-title: A combined multi-criteria approach to support FMECA analyses: a real-world case publication-title: Reliab. Eng. Syst. Saf. – volume: 190 start-page: 577 year: 2018 end-page: 591 ident: b0010 article-title: An integrated fuzzy MOORA method and FMEA technique for sustainable supplier selection considering quantity discounts and supplier's risk publication-title: J. Cleaner Prod. – year: 2003 ident: b0225 article-title: Failure mode and effect analysis: FMEA from theory to execution – volume: 162 start-page: 185 year: 2018 end-page: 201 ident: b0170 article-title: Risk evaluation by FMEA of supercritical water gasification system using multi-granular linguistic distribution assessment publication-title: Knowl.-Based Syst. – volume: 11 start-page: 113 year: 2018 end-page: 132 ident: b0105 article-title: Optimizing a fuzzy green p-hub centre problem using opposition biogeography based optimization publication-title: J. Optim. Ind. Eng. – volume: 33 start-page: 1991 year: 2017 end-page: 2005 ident: b0180 article-title: Optimal harvesting of two species mutualism model with interval parameters publication-title: J. Intell. Fuzzy Syst. – volume: 22 start-page: 986 year: 2005 end-page: 1004 ident: b0210 article-title: Systematic failure mode effect analysis (FMEA) using fuzzy linguistic modelling publication-title: Int. J. Qual. Reliabil. Manage. – volume: 31 start-page: 108 year: 2016 end-page: 118 ident: b0095 article-title: Fuzzy belief structure based VIKOR method: an application for ranking delay causes of Tehran metro system by FMEA criteria publication-title: Transport – volume: 36 start-page: 1768 year: 2009 end-page: 1779 ident: b0045 article-title: Failure mode and effects analysis using a group-based evidential reasoning approach publication-title: Comput. Oper. Res. – reference: Team, R. C. (2000). R language definition. – reference: (Vol. 1). John Wiley & Sons. – volume: 62 start-page: 23 year: 2013 end-page: 36 ident: b0130 article-title: Fuzzy failure mode and effects analysis using fuzzy evidential reasoning and belief rule-based methodology publication-title: IEEE Trans. Reliab. – volume: 187 year: 2019 ident: b0055 article-title: Analysis of operational safety risks in shipbuilding using failure mode and effect analysis approach publication-title: Ocean Eng. – volume: 40 start-page: 828 year: 2013 end-page: 838 ident: b0135 article-title: Risk evaluation approaches in failure mode and effects analysis: a literature review publication-title: Expert Syst. Appl. – volume: 18 start-page: 324 year: 2001 end-page: 336 ident: b0200 article-title: Modified approach for prioritization of failures in a system failure mode and effects analysis publication-title: Int. J. Qual. Reliabil. Manage. – volume: 32 start-page: 795 year: 2016 end-page: 804 ident: b0205 article-title: A dynamic maintenance planning framework based on fuzzy TOPSIS and FMEA: Application in an international food company publication-title: Qual. Reliab. Eng. Int. – volume: 159 start-page: 1638 year: 2016 end-page: 1645 ident: b0035 article-title: ROC-ing along: Evaluation and interpretation of receiver operating characteristic curves publication-title: Surgery – year: 1993 ident: b0090 article-title: Modelling failure modes and effects analysis publication-title: Int. J. Qual. Reliabi. Manage. – year: 2004 ident: b0050 article-title: An introduction to reliability and maintainability engineering – volume: 43 start-page: 962 year: 2014 end-page: 970 ident: b0160 article-title: Estimating predicted probabilities from logistic regression: different methods correspond to different target populations publication-title: Int. J. Epidemiol. – volume: 46 start-page: 359 year: 2005 end-page: 373 ident: b0075 article-title: A fuzzy data envelopment analysis approach for FMEA publication-title: Prog. Nucl. Energy – volume: 136 start-page: 516 year: 2019 end-page: 527 ident: b0240 article-title: Risk assessment based on hybrid FMEA framework by considering decision maker’s psychological behavior character publication-title: Comput. Ind. Eng. – volume: 39 start-page: 2991 year: 2001 end-page: 3002 ident: b0070 article-title: A new approach for evaluation of risk priorities of failure modes in FMEA publication-title: Int. J. Prod. Res. – volume: 26 year: 2018 ident: b0165 article-title: Understanding AUC-ROC Curve. publication-title: Science – volume: 8 start-page: 338 year: 1965 end-page: 353 ident: b0255 article-title: Fuzzy sets publication-title: Inform. Control – volume: 7 start-page: 874 year: 2019 ident: b0040 article-title: A novel FMEA model based on rough BWM and rough TOPSIS-AL for risk assessment publication-title: Mathematics – volume: 4 start-page: 1284373 year: 2017 ident: b0185 article-title: Analyzing the enhancement of production efficiency using FMEA through simulation-based optimization technique: A case study in apparel manufacturing publication-title: Cogent Engineering – volume: 30 start-page: 203 year: 2017 end-page: 218 ident: b0215 article-title: Keep calm and learn multilevel logistic modeling: a simplified three-step procedure Using stata, R, M plus, and SPSS publication-title: Int. Rev. Soc. Psychol. – volume: 125 start-page: 222 year: 2019 end-page: 240 ident: b0235 article-title: An advanced fuzzy Bayesian-based FMEA approach for assessing maritime supply chain risks publication-title: Transport. Res. Part E: Logist. Transport. Rev. – volume: 45 start-page: 979 year: 2018 end-page: 994 ident: b0005 article-title: A quantitative risk analysis by using interval type-2 fuzzy FMEA approach: the case of oil spill publication-title: Marit. Pol. Manage. – reference: Introduction to probability and statistics using r. Lulu. com. – start-page: 1 year: 2019 end-page: 10 ident: b0120 article-title: Advanced FMEA method based on interval 2-tuple linguistic variables and TOPSIS publication-title: Qual. Eng. – volume: 27 start-page: 882 year: 2006 end-page: 891 ident: b0065 article-title: ROC graphs with instance-varying costs publication-title: Pattern Recogn. Lett. – reference: Meel, A., O’neill, L. M., Levin, J. H., Seider, W. D., Oktem, U., Keren, N., 2007. Operational risk assessment of chemical industries by exploiting accident databases. J. Loss Prevent. Process Industr. 20(2), 113–127. – year: 2002 ident: b0115 article-title: Logistic regression – volume: 26 start-page: 1172 year: 2013 end-page: 1186 ident: b0220 article-title: Failure modes and effects analysis using integrated weight-based fuzzy TOPSIS publication-title: Int. J. Comput. Integr. Manuf. – volume: 2 start-page: 1360 year: 2008 end-page: 1383 ident: b0085 article-title: A weakly informative default prior distribution for logistic and other regression models publication-title: Ann. Appl. Statist. – volume: 23 start-page: 549 year: 2010 end-page: 560 ident: b0190 article-title: Harnessing database resources for understanding the profile of chemical process industry incidents publication-title: J. Loss Prev. Process Ind. – volume: 102 start-page: 290 year: 2018 end-page: 300 ident: b0060 article-title: Risk evaluation using a novel hybrid method based on FMEA, extended MULTIMOORA, and AHP methods under fuzzy environment publication-title: Saf. Sci. – reference: Vienna, Austria: R foundation for statistical computing. – reference: Zhang, Z. (2016). Model building strategy for logistic regression: purposeful selection. Ann. Translat. Med. 4(6), 111–111. – volume: 18 start-page: 324 issue: 3 year: 2001 ident: 10.1016/j.ssci.2020.104967_b0200 article-title: Modified approach for prioritization of failures in a system failure mode and effects analysis publication-title: Int. J. Qual. Reliabil. Manage. doi: 10.1108/02656710110383737 – volume: 13 start-page: 441 issue: 2 year: 2019 ident: 10.1016/j.ssci.2020.104967_b0245 article-title: Improving failure mode and effect analysis (FMEA) with consideration of uncertainty handling as an interactive approach publication-title: Int. J. Int. Des. Manufact. (IJIDeM) doi: 10.1007/s12008-018-0496-2 – volume: 7 start-page: 874 issue: 10 year: 2019 ident: 10.1016/j.ssci.2020.104967_b0040 article-title: A novel FMEA model based on rough BWM and rough TOPSIS-AL for risk assessment publication-title: Mathematics doi: 10.3390/math7100874 – volume: 22 start-page: 986 issue: 9 year: 2005 ident: 10.1016/j.ssci.2020.104967_b0210 article-title: Systematic failure mode effect analysis (FMEA) using fuzzy linguistic modelling publication-title: Int. J. Qual. Reliabil. Manage. doi: 10.1108/02656710510625248 – ident: 10.1016/j.ssci.2020.104967_b0265 doi: 10.21037/atm.2016.02.15 – ident: 10.1016/j.ssci.2020.104967_b0150 doi: 10.1016/j.jlp.2006.10.003 – year: 2003 ident: 10.1016/j.ssci.2020.104967_b0225 – volume: 23 start-page: 549 issue: 4 year: 2010 ident: 10.1016/j.ssci.2020.104967_b0190 article-title: Harnessing database resources for understanding the profile of chemical process industry incidents publication-title: J. Loss Prev. Process Ind. doi: 10.1016/j.jlp.2010.05.003 – volume: 45 start-page: 979 issue: 8 year: 2018 ident: 10.1016/j.ssci.2020.104967_b0005 article-title: A quantitative risk analysis by using interval type-2 fuzzy FMEA approach: the case of oil spill publication-title: Marit. Pol. Manage. doi: 10.1080/03088839.2018.1520401 – volume: 159 start-page: 1638 issue: 6 year: 2016 ident: 10.1016/j.ssci.2020.104967_b0035 article-title: ROC-ing along: Evaluation and interpretation of receiver operating characteristic curves publication-title: Surgery doi: 10.1016/j.surg.2015.12.029 – volume: 136 start-page: 516 year: 2019 ident: 10.1016/j.ssci.2020.104967_b0240 article-title: Risk assessment based on hybrid FMEA framework by considering decision maker’s psychological behavior character publication-title: Comput. Ind. Eng. doi: 10.1016/j.cie.2019.07.051 – volume: 187 year: 2019 ident: 10.1016/j.ssci.2020.104967_b0055 article-title: Analysis of operational safety risks in shipbuilding using failure mode and effect analysis approach publication-title: Ocean Eng. doi: 10.1016/j.oceaneng.2019.106214 – volume: 28 start-page: 130 issue: 3 year: 2018 ident: 10.1016/j.ssci.2020.104967_b0020 article-title: An intuitionistic approach based on failure mode and effect analysis for prioritizing corrective and preventive strategies publication-title: Hum. Factors Ergon. Manuf. Serv. Ind. doi: 10.1002/hfm.20729 – volume: 33 start-page: 1991 issue: 4 year: 2017 ident: 10.1016/j.ssci.2020.104967_b0180 article-title: Optimal harvesting of two species mutualism model with interval parameters publication-title: J. Intell. Fuzzy Syst. – volume: 40 start-page: 828 issue: 2 year: 2013 ident: 10.1016/j.ssci.2020.104967_b0135 article-title: Risk evaluation approaches in failure mode and effects analysis: a literature review publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2012.08.010 – volume: 36 start-page: 1768 issue: 6 year: 2009 ident: 10.1016/j.ssci.2020.104967_b0045 article-title: Failure mode and effects analysis using a group-based evidential reasoning approach publication-title: Comput. Oper. Res. doi: 10.1016/j.cor.2008.05.002 – volume: 162 start-page: 185 year: 2018 ident: 10.1016/j.ssci.2020.104967_b0170 article-title: Risk evaluation by FMEA of supercritical water gasification system using multi-granular linguistic distribution assessment publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2018.05.030 – volume: 26 year: 2018 ident: 10.1016/j.ssci.2020.104967_b0165 article-title: Understanding AUC-ROC Curve. Towards Data publication-title: Science – volume: 31 start-page: 761 issue: 5 year: 2015 ident: 10.1016/j.ssci.2020.104967_b0125 article-title: A novel approach for FMEA: Combination of interval 2-tuple linguistic variables and grey relational analysis publication-title: Qual. Reliab. Eng. Int. doi: 10.1002/qre.1633 – volume: 169 start-page: 394 year: 2018 ident: 10.1016/j.ssci.2020.104967_b0030 article-title: A combined multi-criteria approach to support FMECA analyses: a real-world case publication-title: Reliab. Eng. Syst. Saf. doi: 10.1016/j.ress.2017.09.017 – volume: 30 start-page: 203 year: 2017 ident: 10.1016/j.ssci.2020.104967_b0215 article-title: Keep calm and learn multilevel logistic modeling: a simplified three-step procedure Using stata, R, M plus, and SPSS publication-title: Int. Rev. Soc. Psychol. doi: 10.5334/irsp.90 – volume: 11 start-page: 113 issue: 1 year: 2018 ident: 10.1016/j.ssci.2020.104967_b0105 article-title: Optimizing a fuzzy green p-hub centre problem using opposition biogeography based optimization publication-title: J. Optim. Ind. Eng. – volume: 39 start-page: 2991 issue: 13 year: 2001 ident: 10.1016/j.ssci.2020.104967_b0070 article-title: A new approach for evaluation of risk priorities of failure modes in FMEA publication-title: Int. J. Prod. Res. doi: 10.1080/00207540110056162 – year: 2004 ident: 10.1016/j.ssci.2020.104967_b0050 – volume: 46 start-page: 359 issue: 3–4 year: 2005 ident: 10.1016/j.ssci.2020.104967_b0075 article-title: A fuzzy data envelopment analysis approach for FMEA publication-title: Prog. Nucl. Energy doi: 10.1016/j.pnucene.2005.03.016 – volume: 25 start-page: 83 issue: 2 year: 2014 ident: 10.1016/j.ssci.2020.104967_b0195 article-title: Quantitative risk allocation in construction projects: a fuzzy-bargaining game approach publication-title: Int. J. Indust. Eng. Product. Res. – volume: 32 start-page: 795 issue: 3 year: 2016 ident: 10.1016/j.ssci.2020.104967_b0205 article-title: A dynamic maintenance planning framework based on fuzzy TOPSIS and FMEA: Application in an international food company publication-title: Qual. Reliab. Eng. Int. doi: 10.1002/qre.1791 – volume: 26 start-page: 1172 issue: 12 year: 2013 ident: 10.1016/j.ssci.2020.104967_b0220 article-title: Failure modes and effects analysis using integrated weight-based fuzzy TOPSIS publication-title: Int. J. Comput. Integr. Manuf. doi: 10.1080/0951192X.2013.785027 – volume: 102 start-page: 290 year: 2018 ident: 10.1016/j.ssci.2020.104967_b0060 article-title: Risk evaluation using a novel hybrid method based on FMEA, extended MULTIMOORA, and AHP methods under fuzzy environment publication-title: Saf. Sci. doi: 10.1016/j.ssci.2017.10.018 – year: 1993 ident: 10.1016/j.ssci.2020.104967_b0090 article-title: Modelling failure modes and effects analysis publication-title: Int. J. Qual. Reliabi. Manage. doi: 10.1108/02656719310040105 – volume: 4 start-page: 1284373 issue: 1 year: 2017 ident: 10.1016/j.ssci.2020.104967_b0185 article-title: Analyzing the enhancement of production efficiency using FMEA through simulation-based optimization technique: A case study in apparel manufacturing publication-title: Cogent Engineering doi: 10.1080/23311916.2017.1284373 – volume: 31 start-page: 108 issue: 1 year: 2016 ident: 10.1016/j.ssci.2020.104967_b0095 article-title: Fuzzy belief structure based VIKOR method: an application for ranking delay causes of Tehran metro system by FMEA criteria publication-title: Transport doi: 10.3846/16484142.2016.1133454 – year: 2002 ident: 10.1016/j.ssci.2020.104967_b0115 – start-page: 1 year: 2019 ident: 10.1016/j.ssci.2020.104967_b0120 article-title: Advanced FMEA method based on interval 2-tuple linguistic variables and TOPSIS publication-title: Qual. Eng. – volume: 98 start-page: 113 year: 2017 ident: 10.1016/j.ssci.2020.104967_b0250 article-title: An extension to fuzzy developed failure mode and effects analysis (FDFMEA) application for aircraft landing system publication-title: Saf. Sci. doi: 10.1016/j.ssci.2017.06.009 – volume: 8 start-page: 338 issue: 3 year: 1965 ident: 10.1016/j.ssci.2020.104967_b0255 article-title: Fuzzy sets publication-title: Inform. Control doi: 10.1016/S0019-9958(65)90241-X – ident: 10.1016/j.ssci.2020.104967_b0145 – year: 2000 ident: 10.1016/j.ssci.2020.104967_b0100 – volume: 62 start-page: 23 issue: 1 year: 2013 ident: 10.1016/j.ssci.2020.104967_b0130 article-title: Fuzzy failure mode and effects analysis using fuzzy evidential reasoning and belief rule-based methodology publication-title: IEEE Trans. Reliab. doi: 10.1109/TR.2013.2241251 – volume: 50 start-page: 203 issue: 2 year: 1995 ident: 10.1016/j.ssci.2020.104967_b0015 article-title: Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis publication-title: Reliab. Eng. Syst. Saf. doi: 10.1016/0951-8320(95)00068-D – volume: 125 start-page: 222 year: 2019 ident: 10.1016/j.ssci.2020.104967_b0235 article-title: An advanced fuzzy Bayesian-based FMEA approach for assessing maritime supply chain risks publication-title: Transport. Res. Part E: Logist. Transport. Rev. doi: 10.1016/j.tre.2019.03.011 – volume: 317 start-page: 1068 issue: 10 year: 2017 ident: 10.1016/j.ssci.2020.104967_b0155 article-title: Logistic regression diagnostics: understanding how well a model predicts outcomes publication-title: JAMA doi: 10.1001/jama.2016.20441 – volume: 43 start-page: 962 issue: 3 year: 2014 ident: 10.1016/j.ssci.2020.104967_b0160 article-title: Estimating predicted probabilities from logistic regression: different methods correspond to different target populations publication-title: Int. J. Epidemiol. doi: 10.1093/ije/dyu029 – ident: 10.1016/j.ssci.2020.104967_b0110 – volume: 184 start-page: 364 issue: 2 year: 2005 ident: 10.1016/j.ssci.2020.104967_b0175 article-title: ROC analysis publication-title: Am. J. Roentgenol. doi: 10.2214/ajr.184.2.01840364 – volume: 2 start-page: 1360 issue: 4 year: 2008 ident: 10.1016/j.ssci.2020.104967_b0085 article-title: A weakly informative default prior distribution for logistic and other regression models publication-title: Ann. Appl. Statist. doi: 10.1214/08-AOAS191 – volume: 190 start-page: 577 year: 2018 ident: 10.1016/j.ssci.2020.104967_b0010 article-title: An integrated fuzzy MOORA method and FMEA technique for sustainable supplier selection considering quantity discounts and supplier's risk publication-title: J. Cleaner Prod. doi: 10.1016/j.jclepro.2018.04.167 – ident: 10.1016/j.ssci.2020.104967_b0025 doi: 10.1002/9781118312575 – volume: 73 start-page: 684 year: 2018 ident: 10.1016/j.ssci.2020.104967_b0140 article-title: A novel multiple-criteria decision-making-based FMEA model for risk assessment publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2018.09.020 – volume: 27 start-page: 882 issue: 8 year: 2006 ident: 10.1016/j.ssci.2020.104967_b0065 article-title: ROC graphs with instance-varying costs publication-title: Pattern Recogn. Lett. doi: 10.1016/j.patrec.2005.10.012 – year: 2013 ident: 10.1016/j.ssci.2020.104967_b0080 – ident: 10.1016/j.ssci.2020.104967_b0230 – volume: 9 start-page: 43 issue: 1 year: 1975 ident: 10.1016/j.ssci.2020.104967_b0260 article-title: The concept of a linguistic variable and its application to approximate reasoning-III publication-title: Inf. Sci. doi: 10.1016/0020-0255(75)90017-1 |
| SSID | ssj0001361 |
| Score | 2.5606325 |
| Snippet | •This paper addresses the shortcomings of traditional RPN calculation during failure mode and effect analysis, and proposes a systematic approach for... In order to reduce risks of failure, industries use a methodology called Failure Mode and Effects Analysis (FMEA) in terms of the Risk Priority Number (RPN).... |
| SourceID | proquest crossref elsevier |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 104967 |
| SubjectTerms | Causality Electric power generation Failure analysis Failure modes FMEA Interval number Logistic regression Logit models Machine learning Methodology Power plants Probability Probability of risk of failure Pumps Regression analysis Regression models Risk assessment Risk factors Risk Priority Number (RPN) Risk reduction Safety research Statistical analysis |
| Title | Risk assessment by failure mode and effects analysis (FMEA) using an interval number based logistic regression model |
| URI | https://dx.doi.org/10.1016/j.ssci.2020.104967 https://www.proquest.com/docview/2486552316 |
| Volume | 132 |
| WOSCitedRecordID | wos000582127200017&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1879-1042 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001361 issn: 0925-7535 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3fa9RAEF60FSyIaFWsVtkHH5QjR5pfu_t4yBV_0FJsK_cWNsmuvaPEcklK-987s7PJ1ROLCr6EI1yySb4vO7OTb2YYeyOAJwBrFlQ21UFiYhWoRIugSqIoVFUsQlm5ZhPi8FDOZurIx3Qb105A1LW8ulIX_xVq2AdgY-rsX8A9nBR2wG8AHbYAO2z_CPgvKBbXQ8FN9C-tnqP63LW9cV8LehWH7kuSgJ-5fzCdYIygayhv0VWSWMKVjahryAgNXjWilCFX-PkbaWhraqdz08091haloN68Dkv-M922GvO8FiT_OeqaM1TA193gT1ME_eu8uu4GQc4BRjtcqPp0_Hl8M04RrWs-hgSalVrJRSGxm25KFUvGhuZgKRRYh-TnSZqioL9M-BR7WIwbuKMxDosfrRV1-FgrpH2Mg-FYUYh2OxF32WYkUgVz4ebk43T2abDge7GrsztcnE-2Il3g-ki_c2jWTLvzV04esYd-ocEnRJDH7I6pt9n9Pg-92WYPKGLLKRHtCWuROHxFHF5cc08cjgBzQIF74vCeOPwt0uYdd6SBvbwnDSfScEca3pOGr0jjznn-lJ3uT0_efwh8T46gjCPZBqlVNsuKEh4guNaZDK2xaSFNVmllogLTrkMrysIqAb6hNJUysQaXE7xgGcrSxM_YRv29Ns8Zj7OywFpBYHHipLBSlWAMtE0ErrHjotphe_2DzUtfsB77ppznvTJxkSMYOYKRExg7bDQcc0HlWm79d9rjlfs3ghzJHOh163G7Pbi5f_ObPEowxxuWS9mLfzztS7a1enF22Ua77Mwrdq-8bOfN8rUn6Q_U3662 |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Risk+assessment+by+failure+mode+and+effects+analysis+%28FMEA%29+using+an+interval+number+based+logistic+regression+model&rft.jtitle=Safety+science&rft.au=Bhattacharjee%2C+Pushparenu&rft.au=Dey%2C+Vidyut&rft.au=Mandal%2C+U.K.&rft.date=2020-12-01&rft.pub=Elsevier+Ltd&rft.issn=0925-7535&rft.eissn=1879-1042&rft.volume=132&rft_id=info:doi/10.1016%2Fj.ssci.2020.104967&rft.externalDocID=S0925753520303647 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0925-7535&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0925-7535&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0925-7535&client=summon |