Electrical Equipment State Recognition and Monitoring Technology Based on Deep Learning Algorithm

Abstract

In this study, a framework combining multiple deep learning techniques is proposed for electrical equipment state recognition and monitoring to address the problem that existing methods have limited feature extraction capabilities under high noise and complex working conditions. The adaptability of the model is improved through data augmentation and self-supervised contrastive learning. A hybrid architecture of CNN-BiLSTM and Transformer is designed to extract spatiotemporal features, and the model performance is optimized by combining domain adaptation technology, neural architecture search (NAS), and deformable convolutional network (DCN). The experimental data comes from a large-scale electrical equipment monitoring system in an industrial park in a certain province, covering 15 equipment states and a total of 269,000 multimodal data. The experimental results show that the proposed method is significantly superior to the baseline model in terms of recognition accuracy (95.37%), real-time performance (detection delay of 3.02ms), and cross-domain adaptability (improved by 41.5%), providing an efficient and reliable solution for electrical equipment state monitoring, which has important theoretical and practical application value.