A Multi-Scale Residual Convolutional Neural Network for Fault Diagnosis of Progressive Cavity Pump Systems in Coalbed Methane Wells with Imbalanced and Differentiated Data

Coalbed methane, an abundant clean energy resource in China, is gaining significant attention. Electric submersible progressive cavity pumps, ideal for downhole extraction with high solids content, are vital in coalbed methane operations. Current fault diagnosis research for these pumps mainly relies on machine learning algorithms to identify fault features, but complex working conditions and imbalanced sample distributions challenge these models’ ability to perceive multi-scale and multi-dimensional features. To enhance the model’s perception of deep abnormal data in complex multi-case industrial datasets, this study proposes a deep learning model based on a multi-scale extraction and residual module convolutional neural network. Innovatively, a cross-attention module using global autocorrelation and local cross-correlation is introduced to constrain the multi-scale feature extraction process, making the model better suited to specific and differentiated data environments. Post feature extraction, the model employs Borderline-SMOTE to augment minority class samples and uses Tomek Links for noise removal. These enhancements improve the comprehensive perception of fault types with significant differences in period, amplitude, and dimension, as well as the learning capability for rare faults. Based on field-collected fault data and using enhanced and cleaned features for classifier training, tests on a real industrial dataset show the proposed model achieves an F1 Measure of 90.7%—an improvement of 13.38% over the unimproved model and 9.15–31.64% over other common fault diagnosis models. Experimental results confirm the method’s effectiveness in adapting to extremely imbalanced sample distributions and complex, variable field data characteristics.