{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T23:42:20Z","timestamp":1778802140045,"version":"3.51.4"},"reference-count":62,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T00:00:00Z","timestamp":1746748800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hydro-Qu\u00e9bec"},{"name":"Natural Sciences and Engineering Research Council of Canada (NSERC)"},{"name":"Universit\u00e9 du Qu\u00e9bec \u00e0 Trois-Rivi\u00e8res through the Hydro-Qu\u00e9bec Asset Management Research Chair"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>With the evolution of data collection technologies, sensor-generated data have become the norm. However, decades of manually recorded maintenance data still hold untapped value. Natural Language Processing (NLP) offers new ways to extract insights from these historical records, especially from short, unstructured maintenance texts often accompanying structured database fields. While NLP has shown promise in this area, technical texts pose unique challenges, particularly in preprocessing and manual annotation. This study proposes a novel methodology combining Failure Mode and Effect Analysis (FMEA), a reliability engineering tool, into the NLP pipeline to enhance Named Entity Recognition (NER) in maintenance records. By leveraging the structured and domain-specific knowledge encapsulated in FMEAs, the annotation process becomes more systematic, reducing the need for exhaustive manual effort. A case study using real-world data from a major electrical utility demonstrates the effectiveness of this approach. The custom NER model, trained using FMEA-informed annotations, achieves high precision, recall, and F1 scores, successfully identifying key reliability elements in maintenance text. The integration of FMEA not only improves data quality but also supports more informed asset management decisions. This research introduces a novel cross-disciplinary framework combining reliability engineering and NLP. It highlights how domain expertise can be used to streamline annotation, improve model accuracy, and unlock actionable insights from legacy maintenance data.<\/jats:p>","DOI":"10.3390\/make7020042","type":"journal-article","created":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T06:18:51Z","timestamp":1746771531000},"page":"42","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Leveraging Failure Modes and Effect Analysis for Technical Language Processing"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5709-0754","authenticated-orcid":false,"given":"Mathieu","family":"Payette","sequence":"first","affiliation":[{"name":"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"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9550-599X","authenticated-orcid":false,"given":"Georges","family":"Abdul-Nour","sequence":"additional","affiliation":[{"name":"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"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Toualith Jean-Marc","family":"Meango","sequence":"additional","affiliation":[{"name":"Hydro-Qu\u00e9bec\u2019s Research Institute\u2014IREQ, Varennes, QC J3X 1P7, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7857-8070","authenticated-orcid":false,"given":"Miguel","family":"Diago","sequence":"additional","affiliation":[{"name":"Hydro-Qu\u00e9bec\u2019s Research Institute\u2014IREQ, Varennes, QC J3X 1P7, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alain","family":"C\u00f4t\u00e9","sequence":"additional","affiliation":[{"name":"Hydro-Qu\u00e9bec\u2019s Research Institute\u2014IREQ, Varennes, QC J3X 1P7, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,9]]},"reference":[{"key":"ref_1","first-page":"42","article-title":"Technical language processing: Unlocking maintenance knowledge","volume":"27","author":"Brundage","year":"2021","journal-title":"Manuf. 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