## 1 Introduction
### Existing Body of Knowledge and the State of the Art
Another relevant study, Types and Causes of Conveyor Belt Damage, classified various failure types, including misalignment, wear, and delamination, and proposed predictive maintenance strategies. While valuable for long-term belt monitoring, this research does not offer a intervention strategy. In contrast, the proposed project implements a proactive filtering mechanism, preventing the root causes of belt degradation by eliminating problematic materials before they interact with the conveyor. Fire detection and prevention in industrial conveyor belts have also been widely studied. Traditional fire detection systems, such as smoke and gas sensors, are commonly used along conveyor belts to detect dangerous gases or smoke and trigger alarms [36; 37; 38; 39]. However, these systems are prone to false alarms in dusty environments, reducing worker trust and potentially delaying responses during actual fire incidents. Alternative fire suppression techniques, such as water misting and anti-fire foams [37; 39], mainly focus on firefighting rather than early detection. Additionally, Distributed Temperature Sensing (DTS) systems [40; 41] provide accurate temperature monitoring but lack visual data, making them unsuitable for smaller BECS conveyor setups due to their complex installation and high costs. In contrast, recent studies [42; 43] highlight the advantages of thermal cameras for fire detection. Unlike traditional sensors, thermal cameras generate heat maps that enable precise detection of abnormal heat patterns. The proposed project leverages thermal imaging to detect overheating from ferromagnetic contamination, integrating intelligent remote notifications via Telegram to ensure timely intervention, even in unmanned industrial setups. Studies on conveyor belt misalignment have explored various deep learning models, such as YOLOv5 and ARIMA-LSTM hybrid models, for deviation detection [44; 45]. While highly accurate, these deep learning models demand substantial computational resources, requiring high-power GPUs, which may be cost-prohibitive in industrial environments. Additionally, most studies provide only detection mechanisms without automated correction, whereas the proposed project incorporates deviation detection and correction to maintain optimal belt alignment. The study Analysis of Devices to Detect Longitudinal Tear on Conveyor Belts reviewed mechanical, electromagnetic, optical, and magnetic field-based solutions for tear detection [45; 46]. While effective for monitoring belt conditions, these systems are expensive, complex, and lack intervention capabilities. The present project, however, offers a cost-effective, AI-powered alternative with material sorting and belt protection. Also, a recent study introduced PiVisionSort, a cost-effective material recognition system for conveyor belts that integrates image processing and lightweight machine learning techniques. The system, built on Raspberry Pi, employs OpenCV, K-means clustering, and decision trees to accurately identify and distinguish metals, such as aluminum, copper, and brass, under industrial conditions. Its success in achieving up to 100% accuracy in metal detection while maintaining performance underlines its relevance as a benchmark for lightweight, edge-based solutions in innovative recycling environments [47]. Most existing studies have focused on elemental analysis, post-damage assessment, and predictive maintenance. The proposed project advances the field by integrating AI-driven monitoring and prevention for conveyor belts in BECS systems. The key introduced in this study include machine vision-based belt misalignment correction, thermal imaging for early detection of overheating and fire hazards, deep learning-based material analysis (YOLOv11) to identify shape and size, enhancing separation efficiency and belt longevity, and IoT-enabled remote alerts for proactive system monitoring.