Fault Diagnosis Method for Main Pump Motor Shielding Sleeve Based on Attention Mechanism and Multi-Source Data Fusion

The operating environment of the shielding sleeve of the main pump motor is complex and changeable, and it is affected by various stresses; so, it is prone to bulging, cracking, and wear failure. The space where it is located is narrow, making it difficult to install additional sensors for condition monitoring. The existing methods have difficulty in taking into account the advantages of multiple aspects, such as the in-depth extraction of multi-scale data features, multi-source data fusion, and attention mechanisms, thus failing to achieve fault diagnosis for the failure of the shielding sleeve. Therefore, this paper proposes a fault diagnosis method for the shielding sleeve based on the attention mechanism and multi-source data fusion. The proposed method is suitable for scenarios where the fault characteristics of single data sources are not obvious and multi-scale and multi-source data need to be fused collaboratively. This method takes the measurable data (torque, rotational speed, voltage, and current) of the main pump motor operation as input signals. First, a multi-scale convolutional neural network based on the attention mechanism (AM-MSCNN) is established to extract rich multi-scale features of the data, and the spatial and channel attention mechanisms are used to fuse the multi-scale features. Then, on the basis of the AM-MSCNN, a convolutional neural network structure based on the attention mechanism for multi-scale and multi-source data fusion (AM-MSMDF-CNN) is proposed to further fuse the primary fusion features of different channels of torque, rotational speed, voltage, and current. Finally, the BP algorithm and the cross-entropy loss function are used to conduct fault diagnosis and classification on the fused features to complete the fault diagnosis of the shielding sleeve failure. To verify the effectiveness of the proposed method, experimental verification was carried out using datasets generated by finite element simulation and a small-scale equivalent prototype. By comparing it to methods such as the one-dimensional convolutional neural network (1D-CNN), Bagging Ensemble Learning, Random Forest, and Support Vector Machine (SVM), it was found that for the simulation data and experimental data, the accuracy of the AM-MSMDF-CNN is 5-10% and 10-15% higher than that of the other methods, demonstrating the superiority of the method proposed in this paper.