Leveraging Failure Modes and Effect Analysis for Technical Language Processing

Payette, M., Abdul-Nour, G., Meango, T. J.-M., Diago, M. et Côté, A. (2025). Leveraging Failure Modes and Effect Analysis for Technical Language Processing. Machine Learning and Knowledge Extraction, 7 (2). Article 42. ISSN 2504-4990 DOI 10.3390/make7020042

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Résumé

Abstract

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.

Type de document:	Article
Mots-clés libres:	Artificial intelligence Natural language processing Technical language processing Engineering of asset management Reliability
Date de dépôt:	19 nov. 2025 22:11
Dernière modification:	19 nov. 2025 22:11
Version du document déposé:	Version officielle de l'éditeur
URI:	https://depot-e.uqtr.ca/id/eprint/12404

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