(untitled) (Informatica, 2025)
With the rapid advancement of Industry 4.0, the demand for precise fault diagnosis in automation equipment has become critical to ensure manufacturing continuity. This paper introduces a dynamic weight optimization-based system for real-time monitoring and fault diagnosis of automation equipment, leveraging edge computing technologies. The proposed five-layer architecture (Sensor, Production Line, Edge, Network, Cloud) integrates multi-threading and parallel computing at the edge layer to enhance PLC data processing efficiency by 40%. The core fault diagnosis algorithm, driven by dynamic weight optimization, completes weight updates within 300 milliseconds, achieving a 12% higher diagnostic accuracy than traditional methods (e.g., BP neural network and SVM). Experimental validation using 128,000 normal operation samples and 40,000 fault samples demonstrates a 98.3% fault diagnosis accuracy under complex industrial conditions, statistically supported by paired t-tests (p < 0.05). The system exhibits minimal latency degradation even at high data flows (2000 samples/second) and 4G network environments, outperforming cloud-based architectures in real-time fault detection capabilities. Compatibility tests across six industrial device types further validate its robust performance, making this framework a reliable solution for intelligent fault diagnosis in modern manufacturing systems.