Abstract
Fault diagnosis occurrence and its precise prediction in nuclear power plants are extremely important in avoiding disastrous consequences. The inherent limitations of the current fault diagnosis methods make machine learning techniques and their hybrid methodologies possible solutions to remedy this challenge. This study sought to develop, examine, compare, and contrast three robust machine learning methodologies of adaptive neurofuzzy inference system, long short-term memory, and radial basis function network by modeling the loss of feed water event using RELAP5. The performance indices of residual plots, mean absolute percentage error, root mean squared error, and coefficient of determination were used to determine the most suitable algorithms for accurately diagnosing the loss of feed water transient signatures. The study found out that the adaptive neurofuzzy inference system model outperformed the other schemes when predicting the temperature of the steam generator tubes, the radial basis function network scheme was best suited in forecasting the mass flow rate at the core inlet, while the long short-term memory algorithm was best suited for the estimation of the severities of the loss of the feed water fault.
Highlights
Advances in digital and computer technologies have increased the reliability and efficiencies of systems and processes of most industries including the nuclear industry
Optimized models of adaptive neurofuzzy inference system (ANFIS), long short-term memory (LSTM), and radial basis function network (RBFN) were selected for evaluation of the testing datasets. e performances of the outputs of all the models were assessed through both statistical and graphical approaches
Various machine learning techniques have been evaluated by estimating various critical parameters during a risk significant Loss of Feed Water (LOFW) event. ese three methodologies have exhibited superior prediction capabilities, speedy training and processing abilities, adaptive learning, and excellent display of generalization
Summary
Advances in digital and computer technologies have increased the reliability and efficiencies of systems and processes of most industries including the nuclear industry. E current fault diagnosis systems being used in NPPs have inherent limitations [1], which have necessitated the need to come up with intelligent methods for diagnosing faults In this regard, active research in the field of machine learning algorithms and their hybrid techniques is promising and proving as a sure solution to remedy the challenges being experienced [2, 3]. Various machine learning technologies that have been proposed include using artificial neural network (ANN), deep learning (DL), adaptive neurofuzzy inference system (ANFIS), and support vector regression (SVR). Examples of these studies include a study done by [4] where two deep learning networks of convolution neural network (CNN) and long short-term memory (LSTM) were used simultaneously in the diagnosis of faults.
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