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How to Implement Automotive Fault Diagnosis Using Artificial Intelligence Scheme.

Cihun-Siyong Alex Gong1,2,3, Chih-Hui Simon Su1, Yu-Hua Chen1

  • 1Department of Electrical Engineering, School of Electrical and Computer Engineering, College of Engineering, Chang Gung University, Taoyuan 33302, Taiwan.

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Summary

This study enhances vehicle fault detection and diagnosis (VFDD) using AI. It compares machine learning algorithms for predicting failures in vehicle systems, optimizing models for specific applications.

Keywords:
artificial intelligence (AI)machine learning (ML)micromachinedmodelingreinforcement learningsensorsupervised learningunsupervised learningvehicle fault detection and diagnosis (VFDD)

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Internet of Vehicles (IoV)

Background:

  • Vehicle fault detection and diagnosis (VFDD) is crucial for autonomous applications within the Internet of Vehicles (IoV).
  • Predicting and warning about vehicle system failures is a key demand.

Purpose of the Study:

  • To integrate and compare various machine learning algorithms for VFDD.
  • To identify suitable AI architectures for specific vehicle system failures.
  • To optimize AI models for accurate failure prediction.

Main Methods:

  • Discussion and comparison of supervised learning, unsupervised learning, and reinforcement learning.
  • Evaluation of digital filtering processes based on fault status.
  • Pre-construction, analysis, and optimization of vehicle prediction models with adjusted parameters and sample attributes.

Main Results:

  • Identification of AI algorithm architectures suitable for different system failure conditions.
  • Development of optimized vehicle prediction models tailored to specific failure statuses.
  • Cross-comparison and sorting to obtain appropriate AI failure prediction models.

Conclusions:

  • The study provides a framework for selecting and optimizing AI models for specific vehicle fault detection and diagnosis applications.
  • Tailored AI models enhance the accuracy and reliability of failure prediction in IoV systems.