Power equipment fault detection and recognition technology based on intelligent perception and Augmented Reality
A power equipment fault detection model based on AR-OP-CNN-GRU is designed by combining Augmented Reality(AR)and deep learning to address the issue of difficulty in timely judgment and main-tenance by inspection personnel.Based on AR technology,a comprehensive diagnosis model for power e-quipment faults is constructed from the data layer,service layer,and application layer.Then,adaptive me-dian filtering method is used to denoise the image.The OTSU method and Pulse Coupled Neural Network(PCNN)are combined to quickly extract the fault area.At the same time,a fault recognition algorithm for power equipment is proposed,and a CNN-GRU fault diagnosis model integrating local feature pre-extraction modules is constructed.The numerical analysis results show that the proposed AR-OP-CNN-GRU power e-quipment fault detection model can quickly and accurately detect and identify faults in virtual power equip-ment.
intelligent perceptionAugmented Realitydetection and identificationconvolutional net-workgate control loop uni
万方数据
维普
