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arXiv · 2210.06918

Automotive Multilingual Fault Diagnosis

John Pavlopoulos·Alv Romell·Jacob Curman·Olof Steinert·Tony Lindgren·Markus Borg
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Abstract

Automated fault diagnosis can facilitate diagnostics assistance, speedier troubleshooting, and better-organised logistics. Currently, AI-based prognostics and health management in the automotive industry ignore the textual descriptions of the experienced problems or symptoms. With this study, however, we show that a multilingual pre-trained Transformer can effectively classify the textual claims from a large company with vehicle fleets, despite the task's challenging nature due to the 38 languages and 1,357 classes involved. Overall, we report an accuracy of more than 80% for high-frequency classes and above 60% for above-low-frequency classes, bringing novel evidence that multilingual classification can benefit automotive troubleshooting management.

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John Pavlopoulos, Alv Romell, Jacob Curman, Olof Steinert, Tony Lindgren, Markus Borg. 2022-10-13. Automotive Multilingual Fault Diagnosis. https://arxiv.org/abs/2210.06918

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