Research on Fault Diagnosis and Predictive Maintenance of Electrical Automation System Based on Deep Learning

The increase in system complexity has brought about difficulties in fault diagnosis and challenges to traditional maintenance approaches. This study proposes a deep learning-based method for fault diagnosis and predictive maintenance, leveraging deep learning techniques to achieve real-time monitoring, accurate fault identification, and early maintenance intervention in electrical automation systems. A hybrid model named ConvLSTM-FDPM, which integrates Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN), is designed to handle time-series data and the complex dependencies among devices in electrical automation systems. The experimental results show that the accuracy of ConvLSTM-FDPM model in fault diagnosis task reaches 92.4%, which is significantly better than traditional methods such as support vector machine (SVM) and random forest (RF), and shows higher accuracy in predicting maintenance time, with the mean square error (MSE) of 7.3. This research not only improves the maintenance efficiency of single equipment, but also promotes the collaborative optimization of the whole production chain through data interconnection and intelligent analysis, which provides strong support for the safe and stable operation of electrical automation system and is one of the key technologies to realize intelligent manufacturing and Industry 4.0.