Acoustic signal-based fault diagnosis offers a promising non-contact approach for rotating machinery. However, its practical application is usually affected by environmental noise. This paper presented a Denoising Attention Domain Adversarial Network (DDAN) for the robust fault diagnosis of wheel hub motors under severe noise interference. The proposed framework consists of the following two core modules: a DenseNet-based denoising module that adaptively suppresses background noise while retaining critical fault features, and a Stacked Autoencoder Domain Adversarial Network (SADAN) that integrates channel attention, spatial attention, and multi-head self-attention (MHSA) for refined feature extraction and classification. Such a hierarchical attention mechanism facilitates effective local noise suppression and global dependency capture. Validation on a hub motor fault dataset and publicly available online dataset demonstrates that compared to existing methods, DDAN achieves superior diagnostic accuracy across various noise levels and signal-to-noise ratios, improving SNR from -15.97 dB to 1.24 dB, achieving 82.71% accuracy under low SNR condition, and reaching 84.93% and 83.75% accuracy in cross-domain generalization tests. Furthermore, the comparison of the diagnostic accuracy of audio signals from different acoustic acquisition devices further verifies the practicality and potential of the system in low-cost industrial deployment.