Intelligent diagnostic method for beam pumping units based on torque indicator cards

Abstract

An innovative approach is introduced to the intelligent diagnosis of torque indicator card conditions using electric parameters, effectively addressing the high costs associated with acquiring polished rod indicator cards and the convergence difficulties encountered when converting electrical parameters. The proposed method begins by establishing a library of 15 typical operating conditions, followed by an engineering feature analysis. Key to this approach is the use of the AlexNet model for intelligent recognition training, which successfully enhances model convergence and stability, even with varying sample sizes. This allows for automated recognition and classification of operating conditions through model training. The research outcomes demonstrate that the model achieved a fault diagnosis accuracy of over 95% for beam pumping units. This not only improves the accuracy and efficiency of fault diagnosis in oil well pumping units but also presents a novel and effective approach for intelligent diagnostic applications in oil field extraction, offering substantial practical value.