Sparse decomposition method usually shows poor performance in terms of matching with the fault signal due to the personalized vibration behavior of the bearing, and has some drawbacks especially in practical applications due to the improper setting and selection of dictionary parameters. To address these issues, a novel personalized sparse diagnosis method based on dynamical wavelet dictionary was presented. It lies in the foundation for the idea of finite element model (FEM) technology and sparse decomposition. In order to obtain dictionary atoms, according to the different operating conditions, the FEM is built to generate the vibration signals which accord with the bearings features of faults, and the fault transient shock extracting from vibration signal will be regarded as dictionary atom. The dynamical wavelet analysis dictionary can be constructed via atomic Toplitz transformation. The bearing fault feature frequencies can be extracted by performing sparse decomposition and reconstruction of the signal with the help of orthogonal matching pursuit (OMP). The FEM simulation signal and experiment signal results show that the presented scheme can extract the fault features more effectively than the popular parametric dictionary based on a correlation filtering algorithm ( CFA), fast-kurtogram and the K-SVD self-learning dictionary and has a stability and scalability.