A Recursive Generative Adversarial Denoising Learning Method for Acoustic-Based Gear Fault Diagnosis Under Non-Stationary Noise Interference

Acoustic-based diagnosis (ABD) technology demonstrates promising application prospects for rotating machinery such as gears. However, non-stationary background noise may obscure or distort the target acoustic signal, potentially resulting in misdiagnosis or inadequate diagnosis in practical application. Therefore, preserving the inherent periodicity and sparsity features of mechanical sound signals from non-stationary background noise constitutes a critical challenge to facilitating the effective application of ABD in practical industrial environments. To address the shortcoming, this paper proposes an ABD method based on Recursive Generative Adversarial Denoising (RGAD). Specifically, a Global Window-aware Attention Module (GWAM)-based generator is first designed to reconstruct periodic structural features of gear rotational acoustic signals by adaptively representing non-stationary noise components and recursively capturing global dependencies in the time–frequency domain. Subsequently, a generative adversarial mechanism is established through developing a recursive discriminative architecture, which enables the model to effectively alleviate the vanishing gradients during adversarial learning and recover the texture details of gear acoustic features in a coarse-to-fine manner through progressive guidance. Finally, combined with a fault diagnosis network (FDN), a complete RGAD-based ABD framework is constructed. Experimental results demonstrate that the proposed method effectively suppresses noise components while simultaneously reconstructing the periodic characteristics and fine texture details of gear rotational acoustic signals, thereby significantly improving the accuracy and reliability for gear acoustic diagnosis in real industrial scenarios.