Abstract：In the fault diagnosis of pumping well by the traditional identification methods of indicator diagram, there are two main problems, manual selection of indicator diagram features and low identification accuracy.Based on artificial Intelligence theory, an intelligent identification model of indicator diagram is proposed by the combination of convolution neural network ( CNN) and support vector machine ( SVM) . The features of indicator diagram are automatically extracted by means of convolutional neural network, and according to the extracted deep image features, the results of fault diagnosis can be obtained from support vector machine. The experimental results show that the combination of CNN and SVM not only omits manual selection of indicator diagram features, but also increases the identification accuracy to 99. 71%. The test performance is superior to other identification models. A feasible solution is provided by the proposed model for the rapid and accurate fault diagnosis of pumping well, which is significant for the efficient operation of oil fields.

Keyword：convolutional neural network;support vector machine;indicator diagram identification;fault diagnosis;deep learning;