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Combining Ontology and Large Language Models to Identify Recurring Machine Failures in Free-Text Fields

Bengtsson, Marcus, 1977- (författare)
Mälardalens universitet,Innovation och produktrealisering,Volvo Construction Equipment Operations, Eskilstuna, Sweden; School of Innovation, Design and Engineering, Mälardalen University, Sweden
D'Cruze, Ricky Stanley (författare)
Mälardalens universitet,Akademin för innovation, design och teknik,School of Innovation, Design and Engineering, Mälardalen University, Sweden
Ahmed, Mobyen Uddin, Dr, 1976- (författare)
Mälardalens universitet,Inbyggda system,School of Innovation, Design and Engineering, Mälardalen University, Sweden
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Sakao, Tomohiko, 1969- (författare)
Linköpings universitet,Industriell miljöteknik,Tekniska fakulteten,Department of Management and Engineering, Linköping University, Sweden
Funk, Peter, 1957- (författare)
Mälardalens universitet,Inbyggda system,School of Innovation, Design and Engineering, Mälardalen University, Sweden
Sohlberg, Rickard (författare)
Mälardalens universitet,Inbyggda system,School of Innovation, Design and Engineering, Mälardalen University, Sweden
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 (creator_code:org_t)
IOS Press BV, 2024
2024
Engelska.
Ingår i: Advances in Transdisciplinary Engineering. - : IOS Press BV. - 9781643685106 - 9781643685113 ; , s. 27-38
  • Konferensbidrag (refereegranskat)
Abstract Ämnesord
Stäng  
  • 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.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences (hsv//eng)
NATURVETENSKAP  -- Data- och informationsvetenskap -- Systemvetenskap, informationssystem och informatik (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Information Systems (hsv//eng)

Nyckelord

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

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