Fault diagnosis is of great significance for the maintenance of rotating machinery. Deep learning is an intelligent diagnostic technique that is receiving increasing attention. To address the issues of industrial data with small samples and varying working conditions, a residual convolutional neural network based on the attention mechanism is put forward for the fault diagnosis of rotating machinery. The method incorporates channel attention and spatial attention simultaneously, implementing channel-wise recalibration for frequency-dependent feature adjustment and performing spatial context aggregation across receptive fields. Subsequently, a residual module is introduced to address the vanishing gradient problem of the model in deep network structures. In addition, LSTM is used to realize spatiotemporal feature fusion. Finally, label smoothing regularization (LSR) is proposed to balance the distributional disparities among labeled samples. The effectiveness of the method is evaluated by its application to the vibration signal data from the safe injection pump and the Case Western Reserve University (CWRU). The results show that the method has superb diagnostic accuracy and strong robustness.

Fault diagnosis is critical for ensuring the reliability of reciprocating pumps in industrial settings. However, challenges such as strong noise interference and unbalanced conditions of existing methods persist. To address these issues, this paper proposes a novel fusion framework integrating a multiple-branch residual module and a hybrid attention module for reciprocating pump fault diagnosis. The framework introduces a multiple-branch residual module with parallel depth-wise separable convolution, dilated convolution, and direct mapping paths to capture complementary features across different scales. A hybrid attention module is designed to achieve adaptive fusion of channel and spatial attention information while reducing computational overhead through learnable gate mechanisms. Experimental validation on the reciprocating pump dataset demonstrates that the proposed framework outperforms existing methods, achieving an average diagnostic accuracy exceeding 98% even in low signal-to-noise ratio (SNR = −3 dB) environments. This research provides a new perspective for mechanical fault diagnosis, offering significant advantages in diagnostic accuracy, robustness, and industrial applicability.