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Automotive fault no...
Automotive fault nowcasting with machine learning and natural language processing
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- Pavlopoulos, Ioannis, 1983- (författare)
- Stockholms universitet,Institutionen för data- och systemvetenskap
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- Romell, Alv (författare)
- Lund University, Sweden
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- Curman, Jacob (författare)
- Lund University, Sweden
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- Steinert, Olof (författare)
- Scania CV, Sweden
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- Lindgren, Tony, 1974- (författare)
- Stockholm University,Stockholms universitet,Institutionen för data- och systemvetenskap,Scania CV, Sweden
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- Borg, Markus (författare)
- Lund University,Lunds universitet,Programvarusystem,Institutionen för datavetenskap,Institutioner vid LTH,Lunds Tekniska Högskola,LTH profilområde: AI och digitalisering,LTH profilområden,Software Engineering Research Group,Department of Computer Science,Departments at LTH,Faculty of Engineering, LTH,LTH Profile Area: AI and Digitalization,LTH Profile areas,Faculty of Engineering, LTH
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- Randl, Korbinian, 1991- (författare)
- Stockholm University,Stockholms universitet,Institutionen för data- och systemvetenskap
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- Pavlopoulos, John (författare)
- Stockholm University
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(creator_code:org_t)
- 2024
- 2024
- Engelska.
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Ingår i: Machine Learning. - 0885-6125 .- 1573-0565. ; 113:2, s. 843-861
- Relaterad länk:
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https://doi.org/10.1...
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https://su.diva-port... (primary) (Raw object)
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http://dx.doi.org/10... (free)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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https://lup.lub.lu.s...
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Abstract
Ämnesord
Stäng
- Automated fault diagnosis can facilitate diagnostics assistance, speedier troubleshooting, and better-organised logistics. Currently, most AI-based prognostics and health management in the automotive industry ignore textual descriptions of the experienced problems or symptoms. With this study, however, we propose an ML-assisted workflow for automotive fault nowcasting that improves on current industry standards. We show that a multilingual pre-trained Transformer model can effectively classify the textual symptom claims from a large company with vehicle fleets, despite the task’s challenging nature due to the 38 languages and 1357 classes involved. Overall, we report an accuracy of more than 80% for high-frequency classes and above 60% for classes with reasonable minimum support, bringing novel evidence that automotive troubleshooting management can benefit from multilingual symptom text classification.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Automotive fault nowcasting
- Natural language processing
- Multilingual text classification
- data- och systemvetenskap
- Computer and Systems Sciences
- Automotive fault nowcasting
- Multilingual text classification
- Natural language processing
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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