Data Generation Approach Based on Data Model Fusion: An Application for Rolling Bearings Fault Diagnosis With Small Samples

Utilizing fake data (simulated based on mechanism models or generated through data-driven models) for data enhancement is a popular approach to solve the problem of fault diagnosis with small samples. Consequently, the quality of such fake data impacts fault diagnosis accuracy. This article proposes a data model fusion (DMF)-driven framework for small sample fault diagnosis. This framework integrates the digital twin model (DTM) and the conditional deep convolutional generative adversarial network (C-DCGAN). Digital twin data (DTD) under various fault conditions is first obtained in the data generation stage based on DTM simulation. Then, a data generation method based on DTM-C-DCGAN is proposed. The method adopts DTD as the soft-physics constraint input to the generator of C-DCGAN. Hence, the generator is induced to generate data that is more consistent with the failure mechanism and closer to the real data. During the fault diagnosis stage, the generated data (GD) are used to enhance the training process of the fault diagnosis model, improving its generalization ability. Finally, the effectiveness of the proposed method is comprehensively verified via two publicly rolling bearing datasets. Compared with the existing single data-driven and physics-based methods, the experimental results demonstrate that the proposed DMF method can significantly enhance the quality of the GD and improve the accuracy of fault identification, achieving an average accuracy of 97.31%.