Aiming at the problem of insufficient feature extraction in traditional neural networks under strong noise interference, a new global attention residual shrinkage network is proposed for accurate diagnosis of piston pump faults in complex environments.

First, data segmentation is performed on the original signals. Then, a new global feature extractor with an attention mechanism is established to extract fault-related features from the signals, while a threshold softening mechanism is introduced to minimize noise interference. Back propagation optimization is then performed on the network model to reduce loss and improve the model's diagnostic performance. Finally, the feature extraction results are input into the fault classifier for fault identification. The effectiveness of the proposed method is verified by using a piston pump fault simulation test bed.

The results show that, compared with other models, the established global attention residual shrinkage network model has higher diagnostic accuracy and stronger anti-interference ability.

The proposed method demonstrates accurate fault diagnosis in complex and harsh environments.