In the process of structural health monitoring, effective fault diagnosis of the rotary machinery is crucial to ensure safe and reliable operations. The majority of rotary machinery, however, typically operates under varying operational conditions and is prohibited from operating in the fault state. The fluctuation in these conditions results in a corresponding variation in the distribution of samples, and the difficulty of collecting fault data leads to an imbalance in the number of health and fault samples. Transfer learning provides a promising tool for handling the cross-domain diagnosis problems. However, the potential of transfer learning techniques in motor current analysis as a cost-effective alternative has not yet received sufficient attention.” In this article, a novel deep domain adaptation approach called fault feature proxy transfer (FFPT) is implemented to facilitate knowledge transfer across different operating condition domains in the presence of imbalanced class distribution. The proposed end-to-end scheme automatically learns the fault features from the raw current signals and transfers the fault feature explicitly both considering marginal and conditional distribution discrepancies. In FFPT, a set of proxies are employed to facilitate the transfer of features by establishing an intermediate domain. We introduce the pseudo-labeling-based metric learning to alleviate the problem of class imbalance between health and damage samples. A high accuracy for single and combined faults, a great robustness against class imbalance, and a significant gain in performing knowledge transfer prove the competitive performance of the proposed domain adaptation approach.