In response to the shortcomings of traditional fault detection methods in terms of deployment difficulty, environmental anti-interference capability, and early fault sensitivity, this paper proposes a voiceprint fault detection model based on WSSA-VMD and the improved Swin Transformer. First, a sparrow search algorithm integrated with the whale optimization algorithm (WSSA) is introduced to adaptively optimize the key parameters of variational mode decomposition (VMD), thereby enhancing the denoising performance of the original acoustic signals. Next, the effective intrinsic mode functions obtained from the optimized VMD are selected to reconstruct the signal, which is then converted into Log-Mel spectrograms to serve as high-quality feature inputs. Finally, an improved Swin Transformer model is constructed by incorporating the convolutional block attention module (CBAM) and replacing the original normalization layer with a root mean square normalization method, so as to strengthen the model′s feature extraction capability and training stability. Experimental results demonstrate that the proposed model achieves an overall accuracy of 97.93%, effectively identifying incipient blade faults while exhibiting strong generalization performance.