<![CDATA[Purpose – This study aims to develop and evaluate a real-time IoT-based intelligent monitoring system for drum brake lining wear to overcome the limitations of conventional manual inspection, which is periodic, subjective, and prone to delayed detection of critical wear conditions. Design/methods/approach – The research adopts a Research and Development (R&D) methodology consisting of design, prototyping, and laboratory testing. The system integrates an ESP32 microcontroller with a VL53L0X time-of-flight sensor to measure brake lining thickness in real time. A rule-based classification algorithm is implemented to categorize brake conditions into SAFE, WARNING, and DANGER states. Experimental evaluation was conducted through 15 trials across a thickness range of 1.0–10.0 mm. Performance metrics include accuracy, mean absolute error (MAE), root mean square error (RMSE), and response time. Findings - The system achieved an average measurement accuracy of 96.0%, MAE of 0.13 mm, and RMSE of 0.15 mm. All samples were correctly classified, resulting in 100% classification accuracy across the three condition states. The system also recorded a mean response time of 1.27 seconds, indicating fast and reliable real-time performance under controlled conditions. Research implications/limitations – The system is feasible for low-cost brake wear monitoring, but validation is limited to laboratory conditions with a small dataset, and real-world factors were not examined. Originality/value – This study presents an integrated IoT-based drum brake monitoring framework combining ToF sensing, embedded rule-based intelligence, and mobile notification in a single low-cost system. It specifically addresses drum brake applications, which remain underexplored compared to disc brake monitoring systems, offering a practical solution for resource-constrained environments.]]>
Copyrights © 2026
