<![CDATA[This research presents the development of a web-based expert system for diagnosing engine faults in Peugeot 406 vehicles, addressing the challenge of limited access to model-specific diagnostic tools particularly in remote or underserved regions where authorized service centers are scarce and the resulting difficulty in early identification of mechanical issues. The system employs a forward-chaining inference method coupled with a depth-first search (DFS) algorithm for rule traversal within a structured knowledge base of 25 engine symptoms and 13 fault types, all formulated through expert interviews and literature review. Through IF THEN production rules, users input observable symptoms via an intuitive, non-technical interface to receive preliminary diagnoses. System testing encompassed simulated case studies covering the full spectrum of defined fault types as well as real-world trials with Peugeot 406 owners in workshop settings, demonstrating over 85% consistency with professional mechanic evaluations. These findings underscore the system’s potential to improve diagnostic efficiency, reduce repair costs, and extend vehicle longevity by enabling timely, data-driven interventions. The implementation of this rule-based expert system thus offers an effective solution for empowering vehicle owners in areas with restricted service access, and future enhancements such as integration of real-time OBD and IoT sensor data, multilingual support, and adaptive machine-learning rule updates are expected to further boost accuracy, flexibility, and user reach.]]>
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