A Study on Sucker Rod Pump Fault Diagnosis Based on Transfer Learning with Residual Network

The sucker rod pump, as a core piece of equipment in oilfield exploitation, plays a critical role in ensuring the accuracy of its condition monitoring, which is essential for optimizing oil and gas production efficiency and ensuring safety. Deep learning has become increasingly important in the condition diagnosis of sucker rod pumps. However, due to the complexity of real-world operational conditions and difficulties in obtaining industrial data, fault diagnosis still relies on manual calibration. To address this issue, this paper proposes a transfer learning-based fault diagnosis method for sucker rod pumps, utilizing the ResNet network. Firstly, the raw data of load displacement collected from the oilfield site undergoes preprocessing steps, including data cleaning, normalization, and augmentation, to ensure data quality. Secondly, after noise reduction, the data is used to generate a power diagram, and a pre-trained ResNet model is fine-tuned through transfer learning. Through comparative experiments, the impact of factors such as the freezing of layer parameters on the final results of the transfer learning strategy is analyzed. The proposed method is capable of classifying and predicting 13 different operating conditions, such as wax deposition and insufficient fluid supply, and demonstrates higher accuracy and robustness compared to traditional models trained directly. Experimental results show that the ResNet-18 with FT-high strategy outperforms baseline models, improving accuracy by 11%. Moreover, appropriate data augmentation plays a positive role in helping the model capture key features. This method not only improves the accuracy of fault diagnosis but also provides an efficient, low-cost intelligent solution for practical applications of the model in complex oilfield operating conditions.