<![CDATA[Bulldozer engines are highly susceptible to overheating during intensive operations, which can lead to severe mechanical damage and costly downtime. This study proposes an Internet of Things (IoT)-based overheat detection and automated cooling system to improve preventive maintenance in heavy machinery. The system utilizes a LilyGo-T Beam (ESP32) microcontroller integrated with a calibrated NTC thermistor sensor and LoRa communication operating at 923 MHz for long-range and low-power data transmission. Temperature data are displayed locally and transmitted to the Blynk IoT platform for real-time remote monitoring and notification when critical thresholds are exceeded. The developed prototype consists of a transmitter–receiver unit and an automated response mechanism that triggers both an alarm and a water-sprayer cooling system. Experimental validation included sensor calibration, thermal response testing, LoRa communication range assessment, and evaluation of cooling performance under various operating conditions. The results demonstrate accurate temperature measurement and reliable data transmission up to 950 meters. The automated water sprayer successfully reduced engine temperature from 80°C to 78.99°C, with an average recovery time of 12 minutes and 28 seconds. Cooling efficiency improved from 16.49% to 22.18%, indicating enhanced heat dissipation capability Overall, the proposed system offers a cost-effective, reliable, and scalable solution for preventing engine overheating in heavy equipment. By integrating IoT-based monitoring with automated cooling, this approach enhances operational performance, minimizes downtime, and extends engine lifespan.]]>
