Research on intelligent operation and maintenance fault prediction and diagnosis system based on deep learning

Electrical control systems are an indispensable component of modern industry and daily life, and their stable operation is crucial for ensuring equipment safety and improving efficiency. However, due to the complexity of the system and the uncertainty of the environment, failures are inevitable. Traditional troubleshooting methods often rely on expert knowledge and experience, which makes them inadequate when dealing with complex systems. In recent years, deep learning algorithms have shown great potential in the field of fault diagnosis due to their powerful feature learning and pattern recognition capabilities. This article systematically elaborates on the basic process of troubleshooting electrical control systems, and provides a detailed introduction to fault diagnosis and prediction methods based on deep learning algorithms, including the application of deep learning models, deep belief networks, and convolutional neural networks. The effectiveness and practicality of these methods in fault diagnosis have been verified through practical testing.