How to Implement Automotive Fault Diagnosis Using Artificial Intelligence Scheme.

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.