Automotive Multilingual Fault Diagnosis

10/13/2022
by   John Pavlopoulos, et al.
0

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 above-low-frequency classes, bringing novel evidence that multilingual classification can benefit automotive troubleshooting management.

READ FULL TEXT
research
06/17/2021

X-FACT: A New Benchmark Dataset for Multilingual Fact Checking

In this work, we introduce X-FACT: the largest publicly available multil...
research
10/03/2022

SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis

We propose MINT, a new Multilingual INTimacy analysis dataset covering 1...
research
04/22/2020

Semantic Entity Enrichment by Leveraging Multilingual Descriptions for Link Prediction

Most Knowledge Graphs (KGs) contain textual descriptions of entities in ...
research
04/24/2023

The State of the Art in transformer fault diagnosis with artificial intelligence and Dissolved Gas Analysis: A Review of the Literature

Transformer fault diagnosis (TFD) is a critical aspect of power system m...
research
10/29/2021

Handshakes AI Research at CASE 2021 Task 1: Exploring different approaches for multilingual tasks

The aim of the CASE 2021 Shared Task 1 (Hürriyetoğlu et al., 2021) was t...
research
05/23/2020

SoC Memory Management for Reducing Fault Problem from Reserved Memory Components

In this paper, the author proposes an optimal management for system on c...

Please sign up or login with your details

Forgot password? Click here to reset