A Data-Driven Fault Prediction Method for Nuclear Power Systems Based on End-to-End Deep Learning Framework - Lu Chao; Chunbing Wang; Shuai Chen; Qizhi Duan; Hongyun Xie (Science and Technology of Nuclear Installations)
With the increase in system complexity and operational performance requirements, nuclear energy systems are developing in the direction of intelligence and unmanned, which also requires a higher demand for its safety so that intelligent fault diagnosis and prediction have become a technology that nuclear power plants need to develop at present. At the same time, due to the rapid development of deep learning technology, it has become a meaningful development direction to predict the fault state of nuclear power plants within the framework of supervised deep learning. Usually, the network structure model used in fault diagnosis and prediction requires professional design, which may cost a lot of time and make it difficult to achieve optimal results. For this purpose, we present an end-to-end deep network for nuclear power system prediction (EDN-NPSP), which can automatically mine the transient features of various detection data in the NPS at the current moment through heterogeneous convolution kernels that can increase the receptive field and then predict the feature evolution results of the NPS in the future through a special deep CNN. The results provide an assessment of the future state of NPS. Based on EDN-NPSP presented in this work, we can avoid complicated manual feature extraction and provide the predicted state directly and rapidly. It will provide operators with useful prediction information and enhance the nuclear energy system fault prediction capabilities.