Article
AI-BASED INDUSTRIAL FAULT DETECTION AND IOT MONITORING SYSTEM USING FPGA
Industrial automation has become an essential component of modern manufacturing systems, where continuous equipment monitoring and rapid fault detection are critical for ensuring operational reliability, worker safety, and production efficiency. Unexpected equipment failures can lead to production downtime, increased maintenance costs, reduced product quality, and potential safety hazards. Conventional fault detection systems generally rely on periodic inspections or standalone monitoring devices, which often fail to identify faults at an early stage and provide limited remote monitoring capabilities. Recent advancements in Field Programmable Gate Arrays (FPGA), Internet of Things (IoT), embedded systems, and cloud computing have enabled the development of intelligent industrial monitoring systems capable of highspeed fault detection and real-time remote supervision. This project presents an FPGA-Based Industrial Fault Detection Using IoT, designed to continuously monitor industrial equipment and rapidly detect abnormal operating conditions. The proposed framework integrates an FPGA controller, temperature, vibration, current, voltage, and gas sensors, WiFi communication, IoT cloud platform, and intelligent fault analysis algorithms into a unified industrial monitoring system. The FPGA processes multiple sensor inputs simultaneously through parallel hardware architecture, enabling rapid fault identification with minimal processing delay. The monitored data are transmitted to the IoT cloud platform for real-time visualization, historical storage, and remote monitoring. Whenever abnormal operating conditions such as overheating, excessive vibration, abnormal current, voltage fluctuations, or gas leakage are detected, the system immediately generates warning alerts and notifies maintenance personnel through cloud-based communication. Experimental evaluation demonstrates accurate fault detection, high-speed FPGA processing, reliable IoT communication, low response time, and stable system performance under different industrial operating conditions. The proposed framework significantly improves industrial safety, minimizes equipment downtime, supports predictive maintenance, reduces maintenance costs, and contributes to the development of intelligent Industry 4.0 manufacturing systems.
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