Resonance-aware digital twin-driven data augmentation for bearing fault diagnosis under sample imbalance

Fault diagnosis and health monitoring of rolling bearings are crucial for ensuring the safe and reliable operation of mechanical equipment. However, the scarcity of labeled fault samples compared to the abundance of healthy data results in severe data imbalance, which fundamentally constrains the performance of data-driven intelligent diagnostic models. To address this issue, this paper proposes a resonance-aware digital twin framework designed for interpretable data augmentation under imbalanced conditions. First, a resonance feature-driven digital twin parameter optimization method is introduced, which adaptively extracts the optimal resonance band from a limited number of measured fault signals and utilizes this information to optimize the parameters of the dynamic model. This calibration process significantly minimizes the discrepancy in resonance characteristics between simulated and measured signals. Second, to address the issue of multi-source mechanical interference and noise in measured signals, a digital twin-based denoising autoencoder is proposed. This approach mixes simulated fault signals with measured healthy signals to replicate real operating conditions, training the encoder to suppress interference noise outside the resonance band and enhance fault features. Experimental results on both publicly available and private datasets validate the effectiveness of the proposed method. The method achieves high fault diagnosis accuracy under extreme imbalance scenarios of a 100:1 ratio (with only five fault samples), with respective accuracy of 92.25 and 97.46%, effectively demonstrating its potential for practical fault diagnosis applications.