Abstract：When the mechanical equipment is running, the monitoring data of rolling bearing have large distribution differences and vibration signals with labels are difficult to be obtained under different conditions, which leads to the low accuracy of existing models. In order to solve the problem of cross working condition diagnosis of rolling bearing, this paper proposes a fault diagnosis method based on domain adaptive. Firstly, the fault sensitive features of vibration signal are extracted and embedded into Grassmann manifold space to avoid feature distortion in the migration process. Secondly, dynamic distribution alignment is used to adaptively adjust the difference of distribution between source domain and target domain. Moreover, by increasing the regularization term of within-class scatter, the distance within the class is minimized and the separability of the class is increased. Finally, according to the principle of structural risk minimization, the domain invariant classification model is established. Compared with the existing methods, the proposed method can effectively improve the classification accuracy.
2021, Vol. 42 
