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Combining Ontology ...
Combining Ontology and Large Language Models to Identify Recurring Machine Failures in Free-Text Fields
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- Bengtsson, Marcus, 1977- (författare)
- Mälardalens universitet,Innovation och produktrealisering,Volvo Construction Equipment Operations, Eskilstuna, Sweden; School of Innovation, Design and Engineering, Mälardalen University, Sweden
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- D'Cruze, Ricky Stanley (författare)
- Mälardalens universitet,Akademin för innovation, design och teknik,School of Innovation, Design and Engineering, Mälardalen University, Sweden
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- 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
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- Funk, Peter, 1957- (författare)
- Mälardalens universitet,Inbyggda system,School of Innovation, Design and Engineering, Mälardalen University, Sweden
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- 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.
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Ingår i: Sustainable Production Through Advanced Manufacturing, Intelligent Automation And Work Integrated Learning, Sps 2024. - : IOS Press BV. - 9781643685106 - 9781643685113 ; , s. 27-38
- Relaterad länk:
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https://doi.org/10.3...
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https://urn.kb.se/re...
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https://doi.org/10.3...
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https://urn.kb.se/re...
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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
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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