论文标题
汽车多语言故障诊断
Automotive Multilingual Fault Diagnosis
论文作者
论文摘要
自动故障诊断可以促进诊断援助，更快的故障排除和较好的物流。目前，汽车行业的基于AI的预后和健康管理忽略了经历的问题或症状的文字描述。但是，通过这项研究，我们表明，尽管由于38种语言和1,357堂课，但多语种预训练的变压器可以有效地对具有车队的大型公司的文本主张进行分类。总体而言，我们报告高频类别的准确性超过80％，高频类别的准确性超过60％，这表明多语言分类可以使汽车故障排除管理受益。
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.
