A new fault diagnosis method for reciprocating piston pump based on feature fusion of CNN and transformer encoder

Reciprocating piston pump is an important power equipment in coal mine production, so the research on condition monitoring and fault diagnosis of reciprocating piston pump is of great significance. It is challenging to extract fault information from monitoring data due to the complex underground environment and serious noise. The existing methods have the problems of insensitive feature extraction and low diagnostic accuracy. Based on this, a new fault diagnosis method for reciprocating piston pumps based on feature fusion of convolutional neural network (CNN) and transformer encoder is proposed. In this method, a multi-scale CNN encoder and transformer encoder are used to extract local and global features of signals in parallel, and a multi-scale convolution module is used to improve the diversity of local features. At the same time, before using the transformer encoder to extract global features, patch segmentation of monitoring signals is carried out in combination with the phase of the reciprocating piston pump crankshaft to reduce the influence of data randomness on global features and improve the interpretability of global features. In addition, a feature fusion module is constructed to realize the interaction and fusion of local and global features and improve the comprehensive characterization ability of the device state. The proposed method is applied to the fault diagnosis task of reciprocating piston pump. The experimental results show that the proposed method achieves a diagnostic accuracy of 99.145% ± 0.1576%, demonstrating its excellent performance. This accuracy rate is significantly higher than that of other existing methods, indicating that the proposed method can more accurately diagnose the faults of reciprocating piston pumps.