To address the issue of the currently popular residual network having low accuracy in identifying gearbox faults in complex noise environments, and the slow convergence speed and poor global search capability of the traditional whale optimization algorithm(WOA), this paper proposes an intelligent fault diagnosis method based on the gramian angular difference field （GADF） and a hybrid whale-particle swarm optimization algorithm combined with a CBAM attention mechanism residual network. First, the collected one-dimensional vibration signals are overlap-sampled to obtain sufficient signal samples. Then, the gramian angular difference field encoding technique is used to convert the one-dimensional data into two-dimensional image data, constructing a two-dimensional image dataset under different faults. Artificial noise is added to expand the sample size and verify the impact of noise on the diagnostic method. Next, a CBAM attention mechanism module is added to the traditional ResNet network to enhance useful features and suppress irrelevant features, thus improving the model′s representation capability. The image dataset is then input into the HWP algorithm-optimized CBAM-ResNet model for training. Finally, the trained CBAM-ResNet model is used to classify the spiral bevel gearbox fault dataset, outputting diagnostic results. Experimental results show that this method can accurately identify spiral bevel gearbox faults without manual denoising, achieving an accuracy rate of 100%, and maintaining 95.38% accuracy in complex noise environments. Compared to other methods, it has higher accuracy, faster network convergence speed, and better robustness.