The domain adaptation (DA) model, aiming to solve the task of unlabeled or less-labeled target domain fault classification through the training of labeled source domain fault data, is widely used in the transfer diagnosis task of mechanical faults by unsupervised learning. However, traditional transfer learning models such as deep domain confusion (DDC) and RevGrad still have the problems of high training cost and low classification accuracy. Therefore, this paper proposes a parameter sharing adversarial domain adaptation network (PSADAN). The proposed approach constructs a shared classifier to unify fault classifiers and domain classifiers to reduce the complexity of network structure (i.e. the number of hyperparameters), and adds the CORAL loss for adversarial training to enhance the domain confusion. Meanwhile, an unbalanced adversarial training strategy is proposed for improving the domain confusion ability of the feature extractor, so as to improve the accuracy of transfer diagnosis. The effectiveness and advantage of the proposed method is verified by the planetary gearbox fault transfer diagnosis experiments including several transfer tasks of load condition, speed condition, and measurement point.