Pumping system fault diagnosis based on convolutional neural network model

At present, sucker rod pumping system is commonly used for onland mechanical oil production, and indicator diagram is an important indicator of oil well working condition. In the process of actual production, manual oil well detection is time consuming and laborious due to the large number and wide distribution of pumping wells. In order to improve manual maintenance efficiency and automatic level, a simplified convolutional neural network model was established based on the convolutional neural network Le-Net model, according to the graphic characteristics of indicator diagram. The actual production data were input the machine learning model for training after they were collected and pretreated, and thus the classification model of indicator diagram was established. In addition, the classification was evaluated by using the test set data. The results show that the newly established convolutional neural network model is stable and can provide the classification of higher accuracy by means of data learning. The classification model can treat multi-classification problems stably and does well in the classification of 15 types of faults. What’s more, the evaluation on the test set indicates that the accuracy of the model is up to 92% and its prediction effect can meet the demand of actual oilfield production.