A Balanced Deep Transfer Network for Bearing Fault Diagnosis

In data-driven bearing fault diagnosis, it is unrealistic to obtain enough labeled data, and the data used for training and testing often have different distributions. Existing methods typically address this issue by either marginal distribution adaptation or conditional distribution adaptation. However, most studies fail to consider both distributions simultaneously and overlook the relative importance between them, resulting in suboptimal diagnostic performance. To address this limitation, this article introduces a novel unsupervised domain adaptation network called the balanced deep transfer network (BDTN). BDTN employs a one-dimensional (1-D) convolutional neural network (1-D CNN) as its backbone and leverages the maximum mean discrepancy (MMD) and pseudolabels to map data with distinct marginal and conditional distributions onto the same feature subspace. To ensure practical applicability, a balance factor is proposed to dynamically adjust the relative importance of the marginal distribution adaptation and conditional distribution adaptation. Finally, transfer learning experiments across sensors at different places using the Case Western Reserve University (CWRU) dataset and the axlebox bearing dataset are conducted to validate the effectiveness and superiority of BDTN.