Nuclear power plant (NPP) is a highly complex engineering system which has typical internal feedback and strong component coupling. With these features, most NPP systems have high risk of radioactive release, which makes it essential to perform fault detection (FD) to the NPP systems. To address this challenge, this paper proposes a FD mechanism named characteristic time-series convolutional neural network (CT-CNN) based on principal component analysis (PCA), time-series analysis, and convolutional neural network (CNN) mechanisms. First, the models of NPP FD system are formulated. Then, the PCA mechanism is applied to extract the features of the NPP system. Next, the time-series analysis and CNN approaches are applied to realize FD to the NPP system. With the above mechanisms, the proposed approach has not only shown strong stability and become adaptive to different data set, but also preserves both time and state characteristics of the NPP system. In experiment, it shows the proposed approach can achieve better performance in both detection accuracy and variance than the classic back propagation, LSTM method, and standard CNN algorithms. More significantly, its optimal accuracy can be as high as 99.8%.