{"id":"https://openalex.org/W4410240055","doi":"https://doi.org/10.3390/make7020042","title":"Leveraging Failure Modes and Effect Analysis for Technical Language Processing","display_name":"Leveraging Failure Modes and Effect Analysis for Technical Language Processing","publication_year":2025,"publication_date":"2025-05-09","ids":{"openalex":"https://openalex.org/W4410240055","doi":"https://doi.org/10.3390/make7020042"},"language":"en","primary_location":{"id":"doi:10.3390/make7020042","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make7020042","pdf_url":"https://www.mdpi.com/2504-4990/7/2/42/pdf?version=1746783268","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2504-4990/7/2/42/pdf?version=1746783268","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5032717289","display_name":"Mathieu Payette","orcid":"https://orcid.org/0000-0001-5709-0754"},"institutions":[{"id":"https://openalex.org/I63341726","display_name":"Universit\u00e9 du Qu\u00e9bec \u00e0 Trois-Rivi\u00e8res","ror":"https://ror.org/02xrw9r68","country_code":"CA","type":"education","lineage":["https://openalex.org/I49663120","https://openalex.org/I63341726"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Mathieu Payette","raw_affiliation_strings":["D\u00e9partement de G\u00e9nie Industriel, \u00c9cole d\u2019ing\u00e9nierie, Universit\u00e9 du Qu\u00e9bec \u00e0 Trois-Rivi\u00e8res, Trois-Rivi\u00e8res, QC G8Z 4M3, Canada"],"raw_orcid":"https://orcid.org/0000-0001-5709-0754","affiliations":[{"raw_affiliation_string":"D\u00e9partement de G\u00e9nie Industriel, \u00c9cole d\u2019ing\u00e9nierie, Universit\u00e9 du Qu\u00e9bec \u00e0 Trois-Rivi\u00e8res, Trois-Rivi\u00e8res, QC G8Z 4M3, Canada","institution_ids":["https://openalex.org/I63341726"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049767622","display_name":"Georges Abdul-Nour","orcid":"https://orcid.org/0000-0001-9550-599X"},"institutions":[{"id":"https://openalex.org/I63341726","display_name":"Universit\u00e9 du Qu\u00e9bec \u00e0 Trois-Rivi\u00e8res","ror":"https://ror.org/02xrw9r68","country_code":"CA","type":"education","lineage":["https://openalex.org/I49663120","https://openalex.org/I63341726"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Georges Abdul-Nour","raw_affiliation_strings":["D\u00e9partement de G\u00e9nie Industriel, \u00c9cole d\u2019ing\u00e9nierie, Universit\u00e9 du Qu\u00e9bec \u00e0 Trois-Rivi\u00e8res, Trois-Rivi\u00e8res, QC G8Z 4M3, Canada"],"raw_orcid":"https://orcid.org/0000-0001-9550-599X","affiliations":[{"raw_affiliation_string":"D\u00e9partement de G\u00e9nie Industriel, \u00c9cole d\u2019ing\u00e9nierie, Universit\u00e9 du Qu\u00e9bec \u00e0 Trois-Rivi\u00e8res, Trois-Rivi\u00e8res, QC G8Z 4M3, Canada","institution_ids":["https://openalex.org/I63341726"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051585657","display_name":"Toualith Jean-Marc Meango","orcid":null},"institutions":[{"id":"https://openalex.org/I47099075","display_name":"Hydro-Qu\u00e9bec","ror":"https://ror.org/01nhzsw25","country_code":"CA","type":"government","lineage":["https://openalex.org/I47099075"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Toualith Jean-Marc Meango","raw_affiliation_strings":["Hydro-Qu\u00e9bec\u2019s Research Institute\u2014IREQ, Varennes, QC J3X 1P7, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hydro-Qu\u00e9bec\u2019s Research Institute\u2014IREQ, Varennes, QC J3X 1P7, Canada","institution_ids":["https://openalex.org/I47099075"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063605246","display_name":"Miguel Diago","orcid":"https://orcid.org/0000-0001-7857-8070"},"institutions":[{"id":"https://openalex.org/I47099075","display_name":"Hydro-Qu\u00e9bec","ror":"https://ror.org/01nhzsw25","country_code":"CA","type":"government","lineage":["https://openalex.org/I47099075"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Miguel Diago","raw_affiliation_strings":["Hydro-Qu\u00e9bec\u2019s Research Institute\u2014IREQ, Varennes, QC J3X 1P7, Canada"],"raw_orcid":"https://orcid.org/0000-0001-7857-8070","affiliations":[{"raw_affiliation_string":"Hydro-Qu\u00e9bec\u2019s Research Institute\u2014IREQ, Varennes, QC J3X 1P7, Canada","institution_ids":["https://openalex.org/I47099075"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001067682","display_name":"Alain C\u00f4t\u00e9","orcid":"https://orcid.org/0000-0003-0310-8212"},"institutions":[{"id":"https://openalex.org/I47099075","display_name":"Hydro-Qu\u00e9bec","ror":"https://ror.org/01nhzsw25","country_code":"CA","type":"government","lineage":["https://openalex.org/I47099075"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Alain C\u00f4t\u00e9","raw_affiliation_strings":["Hydro-Qu\u00e9bec\u2019s Research Institute\u2014IREQ, Varennes, QC J3X 1P7, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hydro-Qu\u00e9bec\u2019s Research Institute\u2014IREQ, Varennes, QC J3X 1P7, Canada","institution_ids":["https://openalex.org/I47099075"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5032717289"],"corresponding_institution_ids":["https://openalex.org/I63341726"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":11.4343,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.98083531,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"7","issue":"2","first_page":"42","last_page":"42"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9879999756813049,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10260","display_name":"Software Engineering Research","score":0.9803000092506409,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5570604205131531}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5570604205131531}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/make7020042","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make7020042","pdf_url":"https://www.mdpi.com/2504-4990/7/2/42/pdf?version=1746783268","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},{"id":"pmh:oai:depot-e.uqtr.ca:12404","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make7020042>","pdf_url":"https://depot-e.uqtr.ca/id/eprint/12404/1/ABDUL-NOUR_G_85_ED.pdf","source":{"id":"https://openalex.org/S4306400388","display_name":"Le d\u00e9p\u00f4t institutionnel (Universit\u00e9 du Qu\u00e9bec \u00e0 Trois-Rivi\u00e8res)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I63341726","host_organization_name":"Universit\u00e9 du Qu\u00e9bec \u00e0 Trois-Rivi\u00e8res","host_organization_lineage":["https://openalex.org/I63341726"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"pmh:oai:doaj.org/article:8b6ccc0f8e7d469e976b74a3c8961cb8","is_oa":true,"landing_page_url":"https://doaj.org/article/8b6ccc0f8e7d469e976b74a3c8961cb8","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Machine Learning and Knowledge Extraction, Vol 7, Iss 2, p 42 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/make7020042","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make7020042","pdf_url":"https://www.mdpi.com/2504-4990/7/2/42/pdf?version=1746783268","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320311593","display_name":"Universit\u00e9 du Qu\u00e9bec \u00e0 Trois-Rivi\u00e8res","ror":"https://ror.org/02xrw9r68"},{"id":"https://openalex.org/F4320322199","display_name":"Hydro-Qu\u00e9bec","ror":"https://ror.org/01nhzsw25"},{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4410240055.pdf","grobid_xml":"https://content.openalex.org/works/W4410240055.grobid-xml"},"referenced_works_count":49,"referenced_works":["https://openalex.org/W168564468","https://openalex.org/W639665211","https://openalex.org/W2111734201","https://openalex.org/W2123442489","https://openalex.org/W2153579005","https://openalex.org/W2169818249","https://openalex.org/W2250539671","https://openalex.org/W2251329024","https://openalex.org/W2340117315","https://openalex.org/W2493916176","https://openalex.org/W2579343286","https://openalex.org/W2775411542","https://openalex.org/W2784269499","https://openalex.org/W2801239853","https://openalex.org/W2885243155","https://openalex.org/W2894062218","https://openalex.org/W2915977242","https://openalex.org/W2962739339","https://openalex.org/W2963691697","https://openalex.org/W2975953085","https://openalex.org/W2976845364","https://openalex.org/W2979826702","https://openalex.org/W2982331550","https://openalex.org/W3036170826","https://openalex.org/W3083709058","https://openalex.org/W3112482057","https://openalex.org/W3123715598","https://openalex.org/W3126263370","https://openalex.org/W3158465536","https://openalex.org/W3166904074","https://openalex.org/W3169403294","https://openalex.org/W3179523857","https://openalex.org/W4212902066","https://openalex.org/W4225739243","https://openalex.org/W4229652269","https://openalex.org/W4283768917","https://openalex.org/W4302072479","https://openalex.org/W4307715530","https://openalex.org/W4313420701","https://openalex.org/W4382457010","https://openalex.org/W4386363607","https://openalex.org/W4391994091","https://openalex.org/W4392851869","https://openalex.org/W4399215836","https://openalex.org/W4399799247","https://openalex.org/W4402572648","https://openalex.org/W4408897907","https://openalex.org/W6739901393","https://openalex.org/W6787895063"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"With":[0],"the":[1,11,86,100,108,115,133],"evolution":[2],"of":[3,15,135,163],"data":[4,8,19,126,168],"collection":[5],"technologies,":[6],"sensor-generated":[7],"have":[9],"become":[10],"norm.":[12],"However,":[13],"decades":[14],"manually":[16],"recorded":[17],"maintenance":[18,42,96,159,210],"still":[20],"hold":[21],"untapped":[22],"value.":[23],"Natural":[24],"Language":[25],"Processing":[26],"(NLP)":[27],"offers":[28],"new":[29],"ways":[30],"to":[31,89,198],"extract":[32],"insights":[33,207],"from":[34,39,127,208],"these":[35],"historical":[36],"records,":[37],"especially":[38],"short,":[40],"unstructured":[41],"texts":[43,58],"often":[44],"accompanying":[45],"structured":[46,101],"database":[47],"fields.":[48],"While":[49],"NLP":[50,87],"has":[51],"shown":[52],"promise":[53],"in":[54,63,95,106,158],"this":[55,136],"area,":[56],"technical":[57],"pose":[59],"unique":[60],"challenges,":[61],"particularly":[62],"preprocessing":[64],"and":[65,77,102,150,188,204],"manual":[66,119],"annotation.":[67],"This":[68,178],"study":[69,123],"proposes":[70],"a":[71,81,128,181],"novel":[72,182],"methodology":[73],"combining":[74,185],"Failure":[75],"Mode":[76],"Effect":[78],"Analysis":[79],"(FMEA),":[80],"reliability":[82,156,186],"engineering":[83,187],"tool,":[84],"into":[85],"pipeline":[88],"enhance":[90],"Named":[91],"Entity":[92],"Recognition":[93],"(NER)":[94],"records.":[97],"By":[98],"leveraging":[99],"domain-specific":[103],"knowledge":[104],"encapsulated":[105],"FMEAs,":[107],"annotation":[109],"process":[110],"becomes":[111],"more":[112,173],"systematic,":[113],"reducing":[114],"need":[116],"for":[117],"exhaustive":[118],"effort.":[120],"A":[121],"case":[122],"using":[124,143],"real-world":[125],"major":[129],"electrical":[130],"utility":[131],"demonstrates":[132],"effectiveness":[134],"approach.":[137],"The":[138,161],"custom":[139],"NER":[140],"model,":[141],"trained":[142],"FMEA-informed":[144],"annotations,":[145],"achieves":[146],"high":[147],"precision,":[148],"recall,":[149],"F1":[151],"scores,":[152],"successfully":[153],"identifying":[154],"key":[155],"elements":[157],"text.":[160],"integration":[162],"FMEA":[164],"not":[165],"only":[166],"improves":[167],"quality":[169],"but":[170],"also":[171],"supports":[172],"informed":[174],"asset":[175],"management":[176],"decisions.":[177],"research":[179],"introduces":[180],"cross-disciplinary":[183],"framework":[184],"NLP.":[189],"It":[190],"highlights":[191],"how":[192],"domain":[193],"expertise":[194],"can":[195],"be":[196],"used":[197],"streamline":[199],"annotation,":[200],"improve":[201],"model":[202],"accuracy,":[203],"unlock":[205],"actionable":[206],"legacy":[209],"data.":[211]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":2}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
