Journal ArticleDOI
Hybrid identification of nuclear power plant transients with artificial neural networks
Mark J. Embrechts,S. Benedek +1 more
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TLDR
A novel hybrid neural network methodology is presented which also correctly classifies the unlabeled transients of the Hungarian Paks nuclear power plant simulator and has been proven as the most robust against the misleading recognition of unlabeling malfunctions.Abstract:
Proper and rapid identification of malfunctions (transients) is of premier importance for the safe operation of nuclear power plants. Feedforward neural networks trained with the backpropagation (BP) algorithm are frequently applied to model simulated nuclear power plant malfunctions. The correct identification of unlabeled transients-or transients of the "don't-know" type have proven to be especially challenging. A novel hybrid neural network methodology is presented which also correctly classifies the unlabeled transients. From this analysis the importance for properly accommodating practical aspects such as the drift of electronics elements of a simulator, the digitization of simulated and actual plant signals, and the accumulating errors during numerical integration became obvious. Beside the feedforward neural networks trained with the BP algorithm, many other types of networks and codes were used for finding the best (sensitive and robust) algorithms. Various neural network based models were successfully applied to identify labeled and unlabeled malfunctions of the Hungarian Paks nuclear power plant simulator. The BP and probabilistic methods have been proven as the most robust against the misleading recognition of unlabeled malfunctions.read more
Citations
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Journal ArticleDOI
Applications of fault detection and diagnosis methods in nuclear power plants: A review
Jianping Ma,Jin Jiang,Jin Jiang +2 more
TL;DR: Popularity of FDD applications in NPPs will continuously increase as FDD theories advance and the safety and reliability requirement for NPP tightens.
Journal ArticleDOI
Recurrent Neural Networks Trained With Backpropagation Through Time Algorithm to Estimate Nonlinear Load Harmonic Currents
J. Mazumdar,R.G. Harley +1 more
TL;DR: A recurrent neural network trained with the backpropagation through time training algorithm is used to find a way of distinguishing between the so-called load harmonics and supply harmonics, without disconnecting the load from the network.
Journal ArticleDOI
Transient identification in nuclear power plants: A review
TL;DR: Recent studies revealed that model-based methods are not suitable candidates for transient identification in NPPs and other methods such as hidden Markov model (HMM), and support vector machines (SVM) are considered.
Journal ArticleDOI
An accident diagnosis algorithm using long short-term memory
Jaemin Yang,Jonghyun Kim +1 more
TL;DR: This study proposes an algorithm for accident diagnosis using long short-term memory (LSTM), which is a kind of RNN, which improves the limitation for time reflection.
Journal ArticleDOI
A dynamic neural network aggregation model for transient diagnosis in nuclear power plants
TL;DR: The proposed system using a two level classifier architecture with a DNNA model instead of the conventional single general purpose neural network for fault diagnosis can provide more accurate numerical values other than qualitative approximation for transient's severity.
References
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Neural Networks: A Comprehensive Foundation
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
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Mary C. Seiler,Fritz A. Seiler +1 more
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Nuclear power plant status diagnostics using an artificial neural network
Eric B. Bartlett,Robert E. Uhrig +1 more
TL;DR: Findings show that ANNs can be used to diagnose and classify nuclear power plant conditions with good results.
Journal ArticleDOI
Survey of Artificial Intelligence Methods for Detection and Identification of Component Faults in Nuclear Power Plants
TL;DR: A comprehensive survey of computer-based systems that apply artificial intelligence methods to detect and identify component faults in nuclear power plants is presented.