Deep Convolutional Neural Network-based Method to Classify Fault Images of Substation Equipment
In order to optimize equipment fault image classification utility,improve classification recall rate,and satisfy image classification requirement of complex and diversified equipment with respect to transforming substation,this work made an attempt at design a deep convolutional neural network-based image classification method.The method entails im-age preprocessing for denoising and smoothing,algorithm-based threshold segmentation,the consequent in-depth feature extraction via deep convolutional neural network,and finally fault level determination and classification according to equip-ment relative temperature difference.The application of the proposed method was demonstrated by experiment capable of maintaining satisfactory recall rate with the increase of image sample amount,more accurate in identifying and classifying fault image samples,and superior in terms of generalization ability.
deep convolutional neural networktransforming substationequipmentfaultimageclassification
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