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.
@article{arxiv.2210.06918,
title = {Automotive Multilingual Fault Diagnosis},
author = {John Pavlopoulos and Alv Romell and Jacob Curman and Olof Steinert and Tony Lindgren and Markus Borg},
journal= {arXiv preprint arXiv:2210.06918},
year = {2022}
}