Abstract The complexity and safety requirements for Nuclear power plants (NPP) warrant a reliable fault diagnosis approach. In this paper, we present a fault diagnosis method based on Correlation Analysis and Deep Belief Network. We utilized the feature selection capability of Correlation Analysis for dimensionality reduction and deep belief network for fault identification. We also discussed the implementation of the algorithm and the process of model building that is characteristics of NPP. To illustrate the performance of the proposed fault diagnosis model, we utilized Personal Computer Transient Analyzer (PCTRAN). In addition, we also compared the fault diagnostic results from back-propagation neural network and support vector machine with our method. The results show that the proposed method has obvious advantages over other methods, and would be of profound guiding significance to the fault diagnosis of NPP.