Abstract: In view of limited training samples and strong non-stationarity and nonlinearity for the faults of the transmission chain within spacecraft, we propose a novel intelligent sparsity assisted multi-classifier based diagnostic algorithm of using the dynamic data. Firstly, the dynamic data, from single sensor acquisitions, are decomposed via the fractal wavelet, to generate the conventional dyadic and non-conventional wavelet subscales. The fractal wavelet is constructed based on the nearly translation-invariant complex wavelet bases and the transform of the classical dyadic wavelet is used for extracting the transition-band dynamic features. On the other hand, the decomposed wavelet subscales are further processed by an intelligent indicator of the fault energy ratio based sparsity multi-classifier. This method can describe fault severity based on the characteristic frequencies of the component faults and generate quantitative sparsity indicators. The proposed method can achieve the goal of the automatic fault diagnosis without artificial interferences. A case study based on the spacecraft bearing is employed to validate the effectiveness of the proposed algorithm. The processing results successfully indicate the types of the actual faults in the bearing.