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- Pavlopoulos, Ioannis, 1983-, et al.
(author)
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Automotive fault nowcasting with machine learning and natural language processing
- 2024
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In: Machine Learning. - 0885-6125 .- 1573-0565. ; 113:2, s. 843-861
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Journal article (peer-reviewed)abstract
- 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.
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