Vehicle breakdowns are a growing problem worldwide, often caused by overheating, oil leaks, battery problems, flat tires, fuel system failures, and other issues. These incidents frequently result in delays, safety hazards, and costly repairs. Existing systems mainly focus on locating nearby mechanics but lack self-diagnostic capabilities. This study presents a mobile-based expert system that offers step-by-step repair instructions, troubleshooting flowcharts, and safety guidelines. The system integrates ensemble machine learning models and rule-based inference to empower users to independently diagnose and resolve minor vehicle faults. The system is designed with offline capability and a user-friendly interface, this tool ensures accessibility and reliability, especially in remote areas. Initial testing demonstrated a classification accuracy of 88% in diagnosing common faults, confirming the system’s effectiveness and potential for real-world deployment.
Agostino et al. (Tue,) studied this question.
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