Abnormal Event Identification in Nuclear Power Plants Using a Neural Network and Knowledge Processing

Ohga, Yukiharu; Seki, Hiroshi

doi:10.13182/nt93-a34777

Cited by 48 publications

(5 citation statements)

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“…A fitness measure also needs to be developed to confirm the degree of belief. Besides the improvements described in the improved model, it needs to be integrated with other features such as knowledge processing 38 or neural networks 22 to improve its accuracy.…”

Section: Discussionmentioning

confidence: 99%

Hidden Markov model-based real-time transient identifications in nuclear power plants

Kwon

Kim

Seong

2002

Int. J. Intell. Syst.

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In this article, a transient identification method based on a stochastic approach with the hidden Markov model (HMM) has been suggested and evaluated experimentally for the classification of nine types of transients in nuclear power plants (NPPs). A transient is defined as when a plant proceeds to an abnormal state from a normal state. Identification of the types of transients during an early accident stage in NPPs is crucial for proper action selection. The transient can be identified by its unique time-dependent patterns related to the principal variables. The HMM, a double-stochastic process, can be applied to transient identification that is a spatial and temporal classification problem under a statistical pattern-recognition framework. The trained HMM is created for each transient from a set of training data by the maximum-likelihood estimation method which uses a forward-backward algorithm and the Baum-Welch re-estimation algorithm. The transient identification is determined by calculating which model has the highest probability for given test data using the Viterbi algorithm. Several experimental tests have been performed with normalization methods, clustering algorithms, and a number of states in HMM. There are also a few experimental tests that have been performed, including superimposing random noise, adding systematic error, and adding untrained transients to verify its performance and robustness. The proposed real-time transient identification system has been proven to have many advantages, although there are still some problems that should be solved before applying it to an operating NPP. Further efforts are being made to improve the system performance and robustness in order to demonstrate reliability and accuracy to the required level.

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Section: Discussionmentioning

confidence: 99%

Hidden Markov model-based real-time transient identifications in nuclear power plants

Kwon

Kim

Seong

2002

Int. J. Intell. Syst.

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“…On the other hand, a system that combines a neural network and knowledge processing has been proposed by Ohga and Seki 49 to identify abnormal events that cause a nuclear reactor scram. The change pattern of plant variables in analog form is recognized by the neural network, which is then outputted as the candidate abnormal event.…”

Section: Fault Detection and Identificationmentioning

confidence: 99%

Autonomous Control of Nuclear Power Plants

Basher¹

2003

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“…Dietz et al [7] developed "a method using moving temporal windows by presenting dynamic data and Li et al [8] trained the network". Ohga and Seki [9] used multiple sets of time series data to train the network. Artificial neural networks can diagnose faults through their ability to learn and generalize nonlinear relationships.…”

Section: Introductionmentioning

confidence: 99%

Research Progress of Artificial Neural Network and Its Application in Fault Diagnosis of Chemical Industry

Zhao

Li²

2022

JERR

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Many characteristics exhibited by artificial neural networks, such as nonlinearity, large scale, strong parallel processing ability, as well as robustness, fault tolerance, and strong self-learning ability, make it attractive for fault detection and diagnosis in complex systems. The relationship between the complex process, cumbersome process, and measurable process variable failure causes of chemical process is very complicated. Once a failure occurs, it will cause huge economic losses and casualties. The emergence of artificial neural network provides a new chemical fault diagnosis technology, which can carry out early and accurate fault detection and diagnosis for chemical process and equipment, so as to improve the efficiency and safety of production. This paper introduces the basic principle and development history of artificial neural network, as well as several typical artificial neural networks, such as back propagation algorithm (BP network), radial basis network (RBF network), and their application in chemical process fault diagnosis.

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Abnormal Event Identification in Nuclear Power Plants Using a Neural Network and Knowledge Processing

Cited by 48 publications

References 1 publication

Hidden Markov model-based real-time transient identifications in nuclear power plants

Hidden Markov model-based real-time transient identifications in nuclear power plants

Autonomous Control of Nuclear Power Plants

Research Progress of Artificial Neural Network and Its Application in Fault Diagnosis of Chemical Industry

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