Sökning: WFRF:(Curman Jacob) > (2024) > Automotive fault no...
Fältnamn | Indikatorer | Metadata |
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000 | 04075naa a2200529 4500 | |
001 | oai:DiVA.org:su-222622 | |
003 | SwePub | |
008 | 231013s2024 | |||||||||||000 ||eng| | |
009 | oai:lup.lub.lu.se:e338b887-8afa-40a4-8809-9006de94b4eb | |
024 | 7 | a https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-2226222 URI |
024 | 7 | a https://doi.org/10.1007/s10994-023-06398-72 DOI |
024 | 7 | a https://lup.lub.lu.se/record/e338b887-8afa-40a4-8809-9006de94b4eb2 URI |
040 | a (SwePub)sud (SwePub)lu | |
041 | a engb eng | |
042 | 9 SwePub | |
072 | 7 | a ref2 swepub-contenttype |
072 | 7 | a art2 swepub-publicationtype |
100 | 1 | a Pavlopoulos, Ioannis,d 1983-u Stockholms universitet,Institutionen för data- och systemvetenskap4 aut0 (Swepub:su)iopa3492 |
245 | 1 0 | a Automotive fault nowcasting with machine learning and natural language processing |
264 | 1 | c 2024 |
338 | a electronic2 rdacarrier | |
520 | a 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. | |
650 | 7 | a NATURVETENSKAPx Data- och informationsvetenskapx Datavetenskap0 (SwePub)102012 hsv//swe |
650 | 7 | a NATURAL SCIENCESx Computer and Information Sciencesx Computer Sciences0 (SwePub)102012 hsv//eng |
653 | a Automotive fault nowcasting | |
653 | a Natural language processing | |
653 | a Multilingual text classification | |
653 | a data- och systemvetenskap | |
653 | a Computer and Systems Sciences | |
653 | a Automotive fault nowcasting | |
653 | a Multilingual text classification | |
653 | a Natural language processing | |
700 | 1 | a Romell, Alvu Lund University, Sweden4 aut |
700 | 1 | a Curman, Jacobu Lund University, Sweden4 aut |
700 | 1 | a Steinert, Olofu Scania CV, Sweden4 aut |
700 | 1 | a Lindgren, Tony,d 1974-u Stockholm University,Stockholms universitet,Institutionen för data- och systemvetenskap,Scania CV, Sweden4 aut0 (Swepub:su)tonyn |
700 | 1 | a Borg, Markusu 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, LTH4 aut0 (Swepub:lu)csz-mkb |
700 | 1 | a Randl, Korbinian,d 1991-u Stockholm University,Stockholms universitet,Institutionen för data- och systemvetenskap4 aut0 (Swepub:su)kora8563 |
700 | 1 | a Pavlopoulos, Johnu Stockholm University4 aut |
710 | 2 | a Stockholms universitetb Institutionen för data- och systemvetenskap4 org |
773 | 0 | t Machine Learningg 113:2, s. 843-861q 113:2<843-861x 0885-6125x 1573-0565 |
856 | 4 | u https://doi.org/10.1007/s10994-023-06398-7y Fulltext |
856 | 4 | u https://su.diva-portal.org/smash/get/diva2:1804774/FULLTEXT01.pdfx primaryx Raw objecty fulltext:print |
856 | 4 | u http://dx.doi.org/10.1007/s10994-023-06398-7x freey FULLTEXT |
856 | 4 8 | u https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-222622 |
856 | 4 8 | u https://doi.org/10.1007/s10994-023-06398-7 |
856 | 4 8 | u https://lup.lub.lu.se/record/e338b887-8afa-40a4-8809-9006de94b4eb |
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