Fault Detection Method of Maintenance Equipment Based on ECAT

To improve the fault detection performance of power system operation and maintenance equipment, this paper studies the ECAT model through the method of integrating Empirical Mode Decomposition (EMD), Convolutional Neural Networks (CNN), and Attention mechanisms. The results show that: the ECAT model significantly outperforms traditional methods in fault detection accuracy and robustness on multiple public datasets, with an average accuracy improvement of about 10%; the model parameters are globally optimized using the Zebra Optimization Algorithm (ZOA), further enhancing detection performance; and the model effectively captures critical information and long-range dependencies. It can be seen that ECAT provides an efficient and accurate solution for intelligent fault detection of power system operation and maintenance equipment, demonstrating good application prospects.