The Defect Identification System of Electromechanical Equipment on the Edge Side of the Power Grid under Edge Computing

Abstract

With the development of the industrial Internet of Things, modern industrial systems have developed towards intelligence. Electromechanical Equipment (EE) is essential, and its defect identification is fundamental. Firstly, this research introduces the basic content of Gated Recurrent Unit (GRU), Variational Auto-Encoder (VAE) in Deep Learning (DL), and Edge Computing (EC) to explore the construction of a defect identification system for EE on the edge side of the power grid. Secondly, combined with the advantages of GRU and VAE, a GRU-VAE defect recognition model is proposed. Then, the EC architecture is introduced, and the EE defect identification system based on the GRU-VAE algorithm is constructed. The EC intelligent EE defect identification service system is designed with this as the core. Finally, simulation experiments are carried out using different data sets to verify the performance of the GRU-VAE model. The results show that the GRU-VAE model has higher precision and recall than the separate GRU model and VAE model, and the corresponding F1 value is also higher. The F1 value can reach 0.997 on aperiodic data and 0.966 on periodic data. In addition, the optimal thresholds of different datasets are analyzed, and the relationship between the length of the time window and the model’s performance is studied. When the time window length is 15, the model performance is optimal. This research on the defect identification system of EE on the edge side of the power grid based on EC and DL can provide a new path and inject new vitality into the defect detection of EE.