Nuclear energy plays an important role in global energy supply, especially as a key low-carbon source of power. However, safe operation is very critical in nuclear power plants (NPPs). Given the significant impact of human-caused errors on three serious nuclear accidents in history, artificial intelligence (AI) has increasingly been used in assisting operators with regard to making various decisions. In particular, data-driven AI algorithms have been used to identify the presence of accidents and their root causes. However, there is a lack of an open NPP accident dataset for measuring the performance of various algorithms, which is very challenging. This paper presents a first-of-its-kind open dataset created using PCTRAN, a pre-developed and widely used simulator for NPPs. The dataset, namely nuclear power plant accident data (NPPAD), basically covers the common types of accidents in typical pressurised water reactor NPPs, and it contains time-series data on the status or actions of various subsystems, accident types, and severity information. Moreover, the dataset incorporates other simulation data (e.g., radionuclide data) for conducting research beyond accident diagnosis.
Fault diagnosis plays an important role in complex and safety-critical systems such as nuclear power plants (NPPs). With the development of artificial intelligence (AI), extensive research has been carried out for fast and efficient fault diagnosis based on intelligent methods. This paper presents a review of various AI-based system-level fault diagnosis methods for NPPs. We first discuss the development history of AI. Based on this exposition, AI-based fault diagnosis techniques are classified into knowledge-driven and data-driven approaches. For knowledge-driven methods, we discuss both the early if–then-based fault diagnosis techniques and the current new theory-based ones. The principles, application, and comparative analysis of the representative methods are systematically described. For data-driven strategies, we discuss single-algorithm-based techniques such as ANN, SVM, PCA, DT, and clustering, as well as hybrid techniques that combine algorithms together. The advantages and disadvantages of both knowledge-driven and data-driven methods are compared, illustrating the tendency to combine the two approaches. Finally, we provide some possible future research directions and suggestions.
Knowledge-driven and data-driven methods are the two representative categories of intelligent technologies used in fault diagnosis in nuclear power plants. Knowledge-driven methods have advantages in interpretability and robustness, while data-driven methods have better performance in ease of modeling and inference efficiency. Given the complementarity of the two methods, a combination of them is a worthwhile investigation. In this work, we introduce two new techniques based on Bayesian theory (knowledge-driven) and artificial neural network (data-driven) for fault diagnosis in nuclear power plants. The first approach exploits an integrated technique, Bayesian Neural Network (BNN), which introduces Bayesian theory into the neural network to provide confidence in diagnosis. The second approach, denoted as Simplified Bayesian Network-Artificial Neural Network (SBN-ANN), adopts a hierarchical diagnosis idea, which firstly uses a simplified Bayesian network to diagnose fault types and then a neural network to diagnose the severity of faults. The two new techniques are implemented and verified with simulated faults data of a typical pressurized water reactor. Compared with single-algorithmic diagnostic approaches such as Bayesian network and neural network, the new combinatorial techniques show better performance in diagnostic precision. The results suggest the feasibility to develop the data and knowledge dual-drive technologies for fault diagnosis.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.