Design and Implementation of an IoT-Based Multi-Parameter Radiator Coolant Quality Monitoring System for Predictive Maintenance

Résumé fourni par la source

Purpose – This study develops and evaluates an Internet of Things (IoT)-based multi-parameter monitoring system for assessing radiator coolant quality and supporting predictive maintenance. The system continuously monitors coolant volume, pH, temperature, and turbidity while providing real-time status information and anomaly alerts through a mobile application. Design/methods/approach – The study employed a Research and Development approach involving system design, prototype development, sensor calibration, laboratory validation, and field implementation. The prototype integrated an ESP32 microcontroller with ultrasonic, pH, temperature, and optical sensors. Sensor data were transmitted through Firebase Realtime Database and displayed using a mobile application developed with MIT App Inventor. Validation was performed against calibrated reference instruments, followed by field testing under urban driving conditions and usability evaluation using the System Usability Scale. Findings – The proposed system demonstrated high measurement accuracy across all monitored coolant parameters and maintained reliable real-time communication between the embedded device and mobile application. Field testing showed that the system successfully identified coolant leakage, acidification, and excessive turbidity before noticeable vehicle-performance degradation occurred. The mobile application also demonstrated favorable usability, indicating that the monitoring interface was practical and accessible for users. Research implications/limitations – The proposed system demonstrates potential for supporting real-time coolant-condition monitoring and early maintenance intervention. However, validation was limited to one vehicle, a 14-day testing period, and urban driving conditions. Originality/value – This study integrates four coolant-quality parameters within a unified IoT architecture, offering a more comprehensive predictive-maintenance approach than conventional single-parameter radiator monitoring systems.

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