An Integrated Fuzzy-Rule and Case-Based Reasoning System for Enhanced Automobile Maintenance and Repair

Automated systems have become essential in assisting motorists with vehicle maintenance and repair, yet many still require technician intervention for output validation. This study introduces a novel framework for integrating fuzzy logic with case-based reasoning (CBR) to enhance the reliability of such systems, especially in handling ambiguous cases. The system efficiently retrieves similar cases from a comprehensive case base, applying proven solutions to new problems. In scenarios where no exact match is found, fuzzy logic approximates a viable solution. We tested this framework on 134 real-world cases from Akwa Ibom Transport Company, demonstrating its effectiveness in resolving vehicle issues by leveraging and approximating solutions. This research significantly advances the accuracy and reliability of automated vehicle maintenance systems, offering a more autonomous approach to diagnostics.