## 3 Methods
### Thermal Imaging for Early Detection and Prevention of Conveyor Belt Overheating  
The Internet of Things (IoT) [63; 64; 65] plays a crucial role in modern industrial safety systems, particularly in mitigating fire hazards in BECS conveyor systems. These systems separate non-ferrous metals, such as aluminum and copper, from non-metallic materials. Still, they are susceptible to fire risks when ferromagnetic contaminants like iron inadvertently enter the system. Such materials can become trapped and heated due to friction and the high-speed rotation of the magnetic drum, leading to potentially hazardous thermal buildup. To address this risk, an IoT-enhanced thermal monitoring system is integrated into the conveyor, leveraging thermal cameras, Raspberry Pi, and IoT connectivity for proactive fire prevention. The thermal cameras in this system operate continuously, capturing high-resolution thermal maps of the conveyor belt with pixel-level accuracy. These cameras generate grayscale thermal images, where intensity variations correlate with temperature fluctuations. The OpenCV library [66; 67; 68] is utilized for image preprocessing, including grayscale conversion, Gaussian blurring, and adaptive thresholding, to enhance hotspot detection. A dynamic region-of-interest (ROI) selection mechanism is implemented, ensuring that only critical zones of the conveyor belt are analyzed, thus optimizing computational efficiency. An empirical analysis of normal operating temperatures is conducted to establish a fire risk threshold. The average conveyor belt temperature is 27 \({}^{\circ}\)C, with occasional minor fluctuations within safe operational limits. However, a critical temperature threshold of 40 \({}^{\circ}\)C is set, beyond which automated alarms are triggered. The system also integrates historical temperature data analysis, allowing for the identification of progressive overheating trends that indicate potentially hazardous conditions before they escalate into fires. A two-tiered alarm system is implemented to enhance situational awareness and rapid response capabilities:  
* Local Alarm with Buzzer:
* When an overheating event is detected, a high-decibel buzzer is immediately activated on site. * This ensures that factory personnel receive an instant warning, allowing them to inspect and address the issue before escalation. * The system prioritizes intervention, preventing small anomalies from evolving into critical failures. * Remote Notification:
* A Telegram bot [69; 70] is integrated with the system using Telegram's secure API. * The bot sends alerts, including temperature readings, thermal snapshots, and timestamped event logs, directly to designated technicians and safety supervisors. * This approach allows for remote monitoring from any location, reducing reliance on on-site personnel and enhancing operational efficiency. * Security measures ensure data privacy, with no personal data storage, and messages are transmitted via end-to-end encrypted channels. Beyond immediate fire prevention, this system is critical in predictive maintenance. By leveraging thermal imaging data, the system continuously learns from detected anomalies, thereby identifying recurring thermal hotspots that may serve as early indicators of potential mechanical issues. These hotspots may signal friction-induced wear and tear in conveyor components, suggesting excessive stress on certain system parts. Additionally, thermal patterns can reveal misaligned conveyor belts, where uneven tension distribution leads to localized heat buildup, which, if left unaddressed, could degrade the belt material and disrupt operational stability. Another critical issue identified through this system is the accumulation of ferromagnetic debris near the magnetic drum, which can interfere with the efficiency of the eddy current separation process and pose further overheating risks.