Abstract
To address the limitation that a single sensor is insufficient for comprehensively extracting deep fault features in strong industrial noise environments, which constrains bearing diagnosis accuracy, this paper proposes an acoustic-vibration collaborative fusion network. First, an Adaptive Gated Residual Block (AGRB) is designed and combined with a Twin-Gated Residual Block (TGRB) architecture to effectively extract highly robust deep local acoustic and vibration features amidst strong background noise. Second, a Bidirectional Attention Sensing Module (BASM) is constructed to perform deep interaction and complementary calibration of heterogeneous acoustic-vibration features in the global semantic dimension, breaking through the limitations of traditional shallow concatenation of multimodal features. To verify the effectiveness of the proposed model, an experimental study was conducted on a 6205 deep groove ball bearing using a non-contact acoustic-vibration synchronous acquisition system with a 25 cm acoustic monitoring distance and a 5096 Hz sampling rate. The dataset contains nine diagnostic categories, including one healthy state and eight fault states.Experimental results indicate that this method can achieve deep dynamic alignment of heterogeneous data. The VA-DFN demonstrates exceptional noise-resistant robustness under varying signal-to-noise ratio (SNR) conditions from −6 dB to 2 dB, achieving a maximum diagnostic accuracy of 99.55%, which is significantly superior to existing single-modality and conventional deep learning baseline models.