DDGF-Net: A Novel Dual-Domain Generative-Discriminative Fusion Network for Gearbox Fault Diagnosis Under Strong Noise Conditions
In industrial scenarios, strong background noise can easily overwhelm the weak impulsive signatures of gearbox faults, leading to time-domain waveform distortion and frequency-domain spectral aliasing, which in turn degrades the feature extraction capability of conventional diagnostic models and significantly reduces their diagnostic accuracy and robustness. To overcome this limitation, a dual-domain generative–discriminative fusion network (DDGF-Net) is proposed for robust gearbox fault diagnosis under strong noise interference. The proposed framework consists of three collaborative components. Firstly, an improved conditional variational autoencoder (CVAE) integrating soft-threshold shrinkage and spectral consistency constraints is designed to perform joint time–frequency denoising and signal reconstruction, thereby preserving subtle fault characteristics while effectively suppressing noise. Secondly, parallel time-domain and frequency-domain encoding branches are constructed to extract transient fault impulses and fault characteristic frequencies, respectively, compensating for the inadequacy of single-domain feature representations. Thirdly, a lightweight bidirectional cross-attention mechanism is introduced to overcome the limitations of conventional fixed-weight fusion strategies, enabling dynamic adjustment of the interaction weights between dual-domain features according to the instantaneous noise intensity, thus maximizing the complementary value of cross-domain features. Extensive comparative experiments conducted on the Southeast University(SEU) gearbox dataset demonstrate that DDGF-Net achieves superior diagnostic performance and noise robustness over eight representative methods, particularly under strong noise interference, thereby validating its effectiveness and superiority in harsh diagnostic scenarios.