Intelligent Diagnosis Technology for Electrical Automation Equipment Oriented to Industrial Internet

With the rapid development of the Industrial Internet, the operating environment of electrical automation equipment has become increasingly complex, and traditional fault diagnosis methods are facing challenges in real-time performance and accuracy. To improve the operational reliability of equipment and the level of intelligent operation and maintenance, this paper proposes an intelligent diagnosis technology for electrical automation equipment oriented to the Industrial Internet. By constructing an edge-cloud collaboration system architecture, efficient access and real-time processing of multi-source data are realized; at the algorithm level, a deep learning-based multi-modal fault diagnosis model is introduced, which integrates heterogeneous information such as current and vibration, and combines transfer learning and remaining useful life (RUL) prediction methods to enhance the diagnosis and prediction capabilities under small-sample conditions. Finally, the system performance is verified through an engineering experiment platform, and the results show that this method has a good application effect in terms of diagnosis accuracy and response timeliness.