Data-Driven Machine Learning for Fault Detection and Diagnosis in Nuclear Power Plants: A Review

Abstract

Data-driven machine learning (DDML) methods for the fault diagnosis and detection (FDD) in the nuclear power plant (NPP) are of emerging interest in the recent years. However, there still lacks research on comprehensive reviewing the state-of-the-art progress on the DDML for the FDD in the NPP. In this review, the classifications, principles, and characteristics of the DDML are firstly introduced, which include the supervised learning type, unsupervised learning type, and so on. Then, the latest applications of the DDML for the FDD, which consist of the reactor system, reactor component, and reactor condition monitoring are illustrated, which can better predict the NPP behaviors. Lastly, the future development of the DDML for the FDD in the NPP is concluded.

Data-driven machine learning for disposal of high-level nuclear waste: A review

“…1, the number of publications as well as the citations has increased rapidly in the last 10 years, and especially in the last 5 years. Combined with the machine learning (ML) methods, data-driven machine learning (DDML) appears to be quite powerful and can handle complex issues without quite much priori knowledge of the system (Hu et al, 2021a).…”

Power Plant Induced-Draft Fan Fault Prediction using Machine Learning Stacking Ensemble

“…Logic regression is a method that makes prediction by analyzing dependent and independent variables by building multi-nominal regression model [33]. The estimator is flexible and less prone to over-fitting [34].…”

Monitoring of Temperature Measurements for Different Flow Regimes in Water and Galinstan with Long Short-Term Memory Networks and Transfer Learning of Sensors

“…The performance of nuclear reactors could be enhanced through automation of monitoring tasks for the early detection of incipient signs of failure in the reactor monitoring system data streams [1][2][3]. Temperature sensing is one of the most common types of measurements in a reactor monitoring system [4].…”

“…An example is the development of virtual sensors through a computational fluid dynamics solution of Navier-Stokes equations [7]. However, the model-based detection of sensor faults is difficult to accomplish because exact knowledge of a complex system, such as a nuclear power plant, is required [1,2]. On the other hand, data-driven methods learn directly from experimental observations without any prior knowledge of the system.…”

“…A number of supervised and unsupervised machine learning (ML) strategies for sensor fault detection in nuclear power plants (NPPs) have been proposed recently. A review of the literature can be found in [1][2][3]. Some specific methods include anomaly detection and discrimination with Hotelling's T 2 test and interquartile range (IQR) method [8], principal component analysis (PCA) [9], enhanced singular value decomposition (ESVD) [10], and correlation analysis and deep belief networks [11].…”