A Nuclear Reactor Accident Diagnosis Technology Integrating Expert Knowledge and Machine Learning Algorithms

The nuclear reactor accident diagnosis is the most important step in the accident handling process, and the diagnosis result directly determines the accident handling strategy. In this paper, a nuclear reactor accident diagnosis method is proposed, which combines expert knowledge and machine learning algorithms. Based on the existing mature expert knowledge, this method introduces machine learning diagnosis algorithms to realize the superposition of advantages and the complementarity of disadvantages of the two methods. In terms of expert knowledge diagnosis, the symptom-oriented accident diagnosis method is adopted to form a symptom-oriented expert knowledge base and an accident diagnosis function module based on expert knowledge; In terms of accident diagnosis based on machine learning algorithm, Extreme Gradient Boosting (XGBoost), Linear Support Vector Machines (SVM), Deep Feedforward Networks (DFN), and Long Short-Term Memory (LSTM) are used to establish the accident diagnosis model, and the voting mechanism algorithm is used to fuse all kinds of algorithms to form the machine learning intelligent diagnosis module. On this basis, this paper puts forward a diagnosis model based on expert knowledge, supplemented by machine learning intelligent diagnosis, and the verification is carried out using the Steam Generator Tube Rupture (SGTR) accident of HPR1000. The results prove the rationality of the method.