Aiming at the problem that the incipient fault characteristics of rolling bearing are weak, and the vibration signal is a group of time-varying sequence, which has a certain time-series correlation, leading to the difficulty of incipient fault detection of rolling bearing, a dynamic independent component analysis fault detection method based on deep decomposition principle (Deep DICA) is proposed in this paper. The main idea is to first increase the observation data matrix in order to take the dynamic process into account. Then, in order to better dig out the weak incipient fault information, the principle of deep decomposition is proposed to extract the features of incipient faults. Finally, a fault detection model is established for online fault detection and the proposed method is verified by bearing experiments. Experimental results show that the proposed fault detection method based on Deep DICA has good accuracy and applicability.