Research on the Architecture Design of Power Equipment Fault Early Warning System

To overcome the limitations of traditional periodic power equipment maintenance—characterized by insufficient scientific basis, low reliability, and poor economic efficiency—this study proposes a big data-driven fault early warning system. Integrating Internet of Things (IoT), Artificial Intelligence (AI), and digital twin technologies, the system enables a shift from reactive maintenance to proactive prevention. Its three-layer architecture includes: (1) a Smart Perception Layer collecting real-time operational data via multi-sensor networks; (2) an Edge Processing Layer performing lightweight preprocessing (filtering/denoising/compression); and (3) an Application Service Layer deploying Recurrent Neural Network (RNN)-based prediction models with dynamic threshold algorithms to forecast fault probability and time-to-failure (TTF) windows. Supported by a hybrid 4G/5G-fiber-ZigBee communication architecture and modular design for secure, adaptable operation, the system demonstrably enhances fault response efficiency, reduces maintenance costs, and strengthens grid resilience.