Citation

Abdul Hamid, Syamsul Bahrin and Embong, Abd Halim and Shukran, Muhammad Syahir Fathuddin and Abdul Kader, Nur Bidayatul Hidayah and Zulkifli, Muhammad Arfan (2026) IIoT-Based Real-Time Monitoring and Predictive Maintenance System for Air Conditioning Ducting Systems. Journal of Engineering Technology and Applied Physics, 8 (2). p. 179. ISSN 2682-8383

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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 withLoRa 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.

Item Type:	Article
Uncontrolled Keywords:	Deep learning, Real-time monitoring
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics > TK7885-7895 Computer engineering. Computer hardware
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	12 Oct 2026 04:08
Last Modified:	12 Oct 2026 04:08
URII:	http://shdl.mmu.edu.my/id/eprint/16911

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