Research on Indicator Diagram Classification Algorithm Based on Improved ResNet
The indicator diagram is an important diagram reflecting the working state of the pumping unit well.By analyzing the closed curve shape of the indicator diagram,the specific working state of the pumping unit well can be obtained,so that whether the fault occurs and the specific fault type can be judged.With the development of deep learning,the classification of indicator diagram based on deep neural network has been gradually applied to the condition detection of pumping wells.We propose a classification algorithm of indicator graph based on improved ResNet.By optimizing residual structure and introducing SE substructure,the classification accuracy and robustness are improved.The improved residual structure is embedded with the SE substructure,which reduces the number of parameters while reducing the dimension of input features,and adds more nonlinear factors while reducing the computational load.By continuously increasing the weight of effective features and continuously reducing the weight of invalid features,the feature re-calibration is completed,which not only accelerates network convergence,but also makes the model more lightweight.Thus the performance of the model is improved.Compared with other models,the improved ResNet model can better adapt to the task of indicator diagram classification,and the classification effect is better.The experimental results show that the improved ResNet indicator graph classification algorithm is superior to other indicator graph classification algorithms in terms of accuracy,recall and Fl value.This study provides a better theoretical support for the condition detection system of pumping unit.
pumping unit wellindicator diagramdeep learningResidual NetworkSqueeze-Excitation substructure
万方数据
维普
