![]() Hence, it is of great importance to timely determine the LOCA situation and evaluate its development. The break of the Primary Heat Transport (PHT) system causes a fast and large loss of coolant, leading to the overheating of the reactor core. Loss of Coolant Accident (LOCA) is a type of severe accident that could happen during the operation of NPPs. A large amount of simulated nuclear power plant data from previous research works has also settled a firm base to carry out AI models for fault diagnosis and post-accident prediction. With the progress of machine learning, especially deep learning, describing accident behavior using data-based Artificial Intelligence (AI) models has become an effective way to avoid the above-mentioned problems. Furthermore, most of the accidents behave as a nonlinear process, which makes the traditional statistical methods difficult to describe the system behavior and development trend. Assumptions have to be often made, whereas the accuracy of the model has to be sacrificed. However, the accident model needed for fault diagnosis and post-accident prediction is hard to construct due to complex physical processes, nonlinear parameter variations, and multiple system factors. The quick and accurate response to a Nuclear Power Plants (NPP) accident is critical to the safety of both the plant and the public. ![]() It then allows NPPs to have an Artificial Intelligence (AI)-based solution for fault diagnosis and post-accident prediction. Such a hybrid model is proved to be functional, accurate, and adaptive, offering quick accident judgment and a reliable decision basis for the emergency response purpose. ![]() The prediction accuracy is enhanced via the collaborative work of CNN and LSTM. The advantages of ConvLSTM, such as effective feature determination and extraction, are applied to the classification of LOCA cases. 2School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an, ChinaĪ combination of Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), and Convolutional LSTM (ConvLSTM) is constructed in this work for the fault diagnosis and post-accident prediction for Loss of Coolant Accidents (LOCAs) in Nuclear Power Plants (NPPs).1College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.Jingke She 1* Tianzi Shi 1 Shiyu Xue 1 Yan Zhu 1 Shaofei Lu 1 Peiwei Sun 2 Huasong Cao 2
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