Abnormal Diagnosis of Pumping Units Based on AGINet Model
In order to overcome the shortcomings of existing abnormal diagnosis methods for pumping units,an AlexNet model with multi-scale and global pooling(abbreviated as AGINet model)has been proposed in this paper.First,the batch normalization was used to replace the original local response normalization in the AGINet model;Sec-ond,the inception module was added;Last,the global average pooling layer was used to replace the original full con-nection layer.Based on the same input,output and thesame dataset of pumping unit indicator diagrams,the training and testing of the AGINet model,AlexNet model,LeNet5 model,VGG11 model,convolutional neural network and sup-port vector machine were completed,respectively.The testing results show that compared with other deep learning models and support vector machine,the classification accuracy,the macro average of Recall,the macro average of F1,the amount of parameters and the size of the memory occupied by the AGINet model have been improved to a certain extent.The specific values are 99.9%,99.9%,99.9%,338649 and 1334 KB,respectively.The AGINet model pro-vides an important technical reference for abnormal diagnosis of pumping units,and promotes the application of ad-vanced computer technologies in the petroleum industry.
Pumping unitAbnormal diagnosisIndicator diagramDeep learning model
NETL
NSTL
万方数据
