Leveraging Transfer Learning for Data Augmentation in Fault Diagnosis of Imbalanced Time-Frequency Images

The rapid advancement of deep learning and time-frequency analysis techniques have brought about a revolution in fault diagnosis for mechanical systems, offering flexible and efficient solutions. Nevertheless, data imbalance issues continue to pose significant obstacles in fault diagnosis modeling. In this research, we propose the use of a domain adaptation generative adversarial network (DAGAN) that capitalizes on transfer learning to extract valuable information from the majority-class data, while concurrently generating and augmenting minority-class data to expand the training dataset. DAGAN incorporates advanced techniques, including deep domain confusion and parameter forgetting, to enhance knowledge extraction and transfer during the transfer learning process, resulting in more realistic and comprehensive generation outcomes when dealing with small sample training. Furthermore, we have developed an imbalanced fault diagnosis method based on DAGAN, which further incorporates Continuous Wavelet Transform and Deep Residual Networks. Finally, the effectiveness and superiority of proposed method are validated on bearing and gearbox datasets. The experimental results demonstrate the outstanding performance of our method in effectively addressing imbalanced fault diagnosis. Note to Practitioners—In this paper, we present a practical approach to enhance the diagnosis of imbalanced faults. Our approach begins by utilizing Time-frequency images, which offer a comprehensive representation of the temporal and spectral characteristics of mechanical systems’ behavior. These images serve as input features and form the foundation for our fault diagnosis modeling. To address the limitations imposed by imbalanced data, we introduce DAGAN and incorporate advanced techniques such as deep domain confusion and parameter forgetting. These techniques facilitate the extraction and transfer of knowledge during the transfer learning process of DAGAN. Consequently, our approach generates more realistic and comprehensive outcomes, even when confronted with limited training samples. To validate the effectiveness and superiority of our proposed approach, we conducted extensive experiments on bearing and gearbox datasets. The results of these experiments demonstrate that our practical approach, which combines wavelet transform-based time-frequency analysis and the innovative DAGAN framework, offers a reliable and comprehensive solution for overcoming challenges associated with imbalanced fault data.