Due to the randomness and complexity of mechanical faults, the new fault modes usually occur unexpectedly in the actual scenarios. In response to the challenges, a three-stage crossing domain intelligent fault diagnosis method is presented in article. Firstly, the partial domain alignment is achieved based on improved target weighted mechanism, and the outlier identifier is constructed to automatically separate the new fault classes. Then, the unsupervised learning model with silhouette coefficient are built to determine the number of new fault categories. Lastly, the simulation signals are further adopted to determine the specific fault categories. Sufficient experiments on the axial piston pump and public bearing datasets validate that the proposed method could predict the number of new fault categories and judge the specific fault categories. The results indicate that the proposed method outperforms the other methods and has promising practical application in fault diagnosis with multiple new faults.