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This paper proposes a novel method namely WaveletKernelNet-Convolutional Block Attention Module-BiLSTM for intelligent fault diagnosis of drilling pumps. Initially, the random forest method is applied to determine the target signals that can reflect the fault characteristics of drilling pumps. Accordingly, the WaveletKernelNet-Convolutional Block Attention Module Net is constructed for noise reduction and fault feature extraction based on signals. The Convolutional Block Attention Module embedded in WaveletKernelNet-CBAM adjusts the weight and enhances the feature representation of channel and spatial dimension. Finally, the Bidirectional Long-Short Term Memory concept is introduced to enhance the ability of the model to process time series data. Upon constructing the network, a Bayesian optimization algorithm is utilized to ascertain and fine-tune the ideal hyperparameters, thereby ensuring the network reaches its optimal performance level. With the hybrid deep learning model presented, an accurate fault diagnosis of a real five-cylinder drilling pump is carried out and the results confirmed its applicability and reliability. Two sets of comparative experiments validated the superiority of the proposed method. Additionally, the generalizability of the model is verified through domain adaptation experiments. The proposed method contributes to the safe production of the oil and gas sector by providing accurate and robust fault diagnosis of industrial equipment. • A novel intelligent drilling pumps fault diagnosis method, WCCN-BiLSTM, is proposed. • The WCCN is constructed for noise reduction and feature extraction of signals • The CBAM module embedded in WCCN enhances the feature representation of channel and spatial dimension. • The diagnostic ability and generalization ability of the model are verified by drilling pump data under different working conditions. [ABSTRACT FROM AUTHOR]

This paper introduces a novel parallel deep neural network for fault diagnosis of drilling pumps. It integrates the Convolutional Block Attention Module with the AlexNet and synchronizes with the Anomaly Transformer model to delve meticulously into both the time and time-frequency domains of signals. The method prioritizes the singular extraction and subsequent amalgamation of features, facilitating a detailed view of diagnostic data and mitigating the risk of interference and overfitting. The integration of the anomaly attention of the Anomaly Transformer with the features of the Convolutional Block Attention Module results in a distinctive dual attention mechanism that is critical to the methodology. This mechanism emphasizes essential features in both the time domain and the time-frequency domain, improving the accuracy of fault diagnosis. Verification with on-site data underscores the preeminence of the approach over existing models, signaling improved reliability and accuracy in diagnosing faults in drilling pumps. This meticulous approach offers promising advances in the study and application of fault diagnosis in energy equipment, demonstrating increased efficiency and refined accuracy. • A novel Parallel Deep Neural Network-Based Fault Diagnosis Framework is proposed. • Time and time-frequency domains of signals are extracted and fused for Fault Diagnosis. • Integrating the Transformer with CBAM to consider the essential features of signals. [ABSTRACT FROM AUTHOR]