Aiming at the problems that fault diagnosis data of wind turbine gearboxes have time series and the single-channel model is difficult to effectively extract the composite fault feature information

a fault diagnosis method integrating an improved orthogonal convolutional capsule network (OCCN) and a bidirectional long short-term memory neural network (BiLSTM) is proposed. First

the original signals are preprocessed. Then

the preprocessed signals are fed into a constructed OCCN-BiLSTM dual-branch model to extract the spatial features and time domain features of composite faults

respectively. Finally

the extracted spatiotemporal features are fused through a cross-attention mechanism and input into a fully connected layer for signal classification

enabling intelligent fault diagnosis of wind turbine gearboxes. Experimental results show that the proposed diagnosis method can effectively achieve intelligent fault diagnosis for wind turbine gearboxes

with an accuracy of 99.53% on the test set.