Digital twin-assisted fault diagnosis framework for rolling bearings under imbalanced data

The application of deep learning-based fault diagnosis methods is constrained by the imbalanced data. Recently, many studies have suggested integrating dynamic model responses into the training process to address data imbalances. However, significant distribution discrepancies exist between dynamic model responses and real measured data, resulting in suboptimal performance. To address this challenge, this research proposes a digital twin-assisted framework for rolling bearings fault diagnosis under imbalanced data, which minimizes the distribution discrepancies between dynamic model responses and real measured data through information and feature transfer. Firstly, a Digital Twin-assisted Data Fusion Strategy (DTDFS) is proposed to facilitate information transfer from physical entities to dynamic models, generating digital twin data for data augmentation. Subsequently, a Frequency Filter Subdomain Adaptation Network (FFSAN) is proposed to achieve feature transfer between twin data and measured data. Finally, experimental results and engineering applications demonstrate that the proposed framework significantly outperforms existing imbalanced fault diagnosis methods, which is crucial to the application of deep learning-based fault diagnosis in industrial settings.