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Combining Ontology and Large Language Models to Identify Recurring Machine Failures in Free-Text Fields
Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation. Volvo Construction Equipment Operations Eskilstuna Sweden, Sweden.ORCID iD: 0000-0002-0729-0122
Mälardalen University, School of Innovation, Design and Engineering.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-3802-4721
Department of Management and Engineering, Linköping University, Sweden.
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2024 (English)In: Advances in Transdisciplinary Engineering, IOS Press BV , 2024, Vol. 52, p. 27-38Conference paper, Published paper (Refereed)
Abstract [en]

Companies must enhance total maintenance effectiveness to stay competitive, focusing on both digitalization and basic maintenance procedures. Digitalization offers technologies for data-driven decision-making, but many maintenance decisions still lack a factual basis. Prioritizing efficiency and effectiveness require analyzing equipment history, facilitated by using Computerized Maintenance Management Systems (CMMS). However, CMMS data often contains unstructured free-text, leading to manual analysis, which is resource-intensive and reactive, focusing on short time periods and specific equipment. Two approaches are available to solve the issue: minimizing free-text entries or using advanced methods for processing them. Free-text allows detailed descriptions but may lack completeness, while structured reporting aids automated analysis but may limit fault description richness. As knowledge and experience are vital assets for companies this research uses a hybrid approach by combining Natural Language Processing with domain specific ontology and Large Language Models to extract information from free-text entries, enabling the possibility of real-time analysis e.g., identifying recurring failure and knowledge sharing across global sites.

Place, publisher, year, edition, pages
IOS Press BV , 2024. Vol. 52, p. 27-38
Keywords [en]
Artificial Intelligence, Experience Reuse, Industrial Maintenance, Large Language Models, Natural Language Processing, Computational linguistics, Decision making, Failure (mechanical), Natural language processing systems, Ontology, Computerized maintenance management system, Free texts, Language model, Language processing, Large language model, Natural languages, Text entry, Maintenance
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-66565DOI: 10.3233/ATDE240151Scopus ID: 2-s2.0-85191305248ISBN: 9781643685106 (print)OAI: oai:DiVA.org:mdh-66565DiVA, id: diva2:1857560
Conference
9 April 2024 11th Swedish Production Symposium, SPS2024. Trollhattan. 23 April 2024 through 26 April 2024
Available from: 2024-05-14 Created: 2024-05-14 Last updated: 2024-05-14Bibliographically approved

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Bengtsson, MarcusD'Cruze, Ricky StanleyAhmed, Mobyen UddinFunk, PeterSohlberg, Rickard

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Bengtsson, MarcusD'Cruze, Ricky StanleyAhmed, Mobyen UddinFunk, PeterSohlberg, Rickard
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Innovation and Product RealisationSchool of Innovation, Design and EngineeringEmbedded Systems
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