The mechanical and electrical performance degradation of satellite components has a serious impact on imaging. How to perform high-precision reconstruction of the monitor data from compressive-sensing (CS) data (limited by online computing, storage and transmission) is often the first stage in the fault diagnosis of in-orbit satellites. In this article, a deep generative model named denoising diffusion probabilistic model (DDPM) is applied for the equipment monitor data reconstruction. The priors-assisted reconstruction method is useful for reducing reconstruction error and decreasing measurement/monitor cost. The reconstruction method mainly consists of unconditional generation transition from pre-trained DDPM noise matching network and conditional likelihood correction step toward downsampling data. An inverse time decay technique is embedded into step size strategy of gradient computation to ensure data consistency. As an unsupervised learning paradigm, the learned deep generative priors can be utilized for measurements with different compressive sampling ratio (CSR) like plug-and-play prior. Numerical experiments executed on control moment gyro (CMG) data and reciprocating refrigeration compressor (RRC) data validate the effectiveness of the new method, in comparison with conventional sparse prior methods and advanced deep learning reconstruction methods. Finally, we conduct out-of-distribution (OOD) generalization experiments on fault working condition, which demonstrates the DDPM priors-assisted data reconstruction method are suitable for different operating conditions.