Sep 2026· Journal of Engineering Technology and Applied Physics· Vol 8, pp. 179· 0 citations· 16 references
TL;DR
Experimental results confirm the system's ability to operate reliably in metallic duct environments, ensuring secure data transmission and delivering accurate AI predictions, as well as a discussion on system performance and future upgrades, including vision-based inspection.
Abstract
This paper presents an Industrial Internet of Things (IIoT)-based real-time monitoring and predictive maintenance system for air-conditioning ducting environments. The proposed system integrates a mobile robotic platform equipped with environmental sensors—BME280 and MQ135—and an ESP32 microcontroller, along with LoRa SX1278 modules for long-range wireless data transmission. The system visualises sensor data through a PyQt5-based graphical user interface. It utilises a deep learning model trained on the author’s datasets to classify environmental conditions into 'Normal,' 'High Humidity,' or 'Fire.' Experimental results confirm the system's ability to operate reliably in metallic duct environments, ensuring secure data transmission and delivering accurate AI predictions. The study concludes with a discussion on system performance and future upgrades, including vision-based inspection.
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