Search: WFRF:(Randl Korbinian 1991 )
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Automotive fault no...
Automotive fault nowcasting with machine learning and natural language processing
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- Pavlopoulos, Ioannis, 1983- (author)
- Stockholms universitet,Institutionen för data- och systemvetenskap
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- Romell, Alv (author)
- Lund University, Sweden
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- Curman, Jacob (author)
- Lund University, Sweden
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- Steinert, Olof (author)
- Scania CV, Sweden
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- Lindgren, Tony, 1974- (author)
- Stockholm University,Stockholms universitet,Institutionen för data- och systemvetenskap,Scania CV, Sweden
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- Borg, Markus (author)
- 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- (author)
- Stockholm University,Stockholms universitet,Institutionen för data- och systemvetenskap
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- Pavlopoulos, John (author)
- Stockholm University
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(creator_code:org_t)
- 2024
- 2024
- English.
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In: Machine Learning. - 0885-6125 .- 1573-0565. ; 113:2, s. 843-861
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Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- 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
Publication and Content Type
- ref (subject category)
- art (subject category)
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