As the important part of power transformer

the management and maintenance of bushing plays the vital role in the safe and stable operation of the equipment. For improving the intelligent level of patrol inspection of electrical equipment

a kind of infrared image recognition method on the fault of bushing based on convolutional neutral networks is proposed in this paper

which has significant advantages in terms of feature extraction and avoids such problem as low efficiency and prone misjudgment due to artificial extraction of descriptive features. Firstly

Infrared image sample database of the bushing at such three states as normal

lack of oil and local overheating is set up.Then

the normalized processed infrared image is used as the input of the convolutional neutral networks and the fault infrared image recognition model of the bushing is set up. Finally

the type of activation function

pooling method and number of convolutional kernel are defined by way of experimental analysis through selection of network hyper parameters. In view of the sample database in the paper

the accuracy rate of classification result of 3 types of status of the bushing by the model proposed in this paper is up to 96% which

compared to SVM algorithm and BP neutral network algorithm

increases by about 14% and 15% respectively and the recognition performance is more superior.