Journal ArticleDOI
Soft computing tools for transient classification
Davide Roverso
- Vol. 127, Iss: 3, pp 137-156
TLDR
How ensembles of recurrent neural networks can overcome some of the limitations encountered in these early prototypes are described, and an example involving the identification of anomalous events in a PWR 900 MW nuclear power plant is given.Abstract:
Any action taken on a process, for example in response to an abnormal situation or in reaction to unsafe conditions, relies on the ability to identify the state of operation or the events that are occurring. Although there might be hundreds or even thousands of measurements in a process, there are generally few events occurring. The data from these measurements must then be mapped into appropriate descriptions of the occurring event(s), which in most cases is a difficult task. A systematic study was carried out with the aim of comparing alternative neural network designs and models for performing this mapping task. Four main approaches have been investigated. Radial basis function (RBF) neural networks and cascade-RBF neural networks combined with fuzzy clustering, self-organizing map neural networks, and recurrent neural networks. The main evaluation criteria adopted were identification accuracy, reliability (i.e., correct recognition of an unknown event as such), robustness (to noise and to changing initial conditions), and real-time performance. Additionally, in this paper we describe how ensembles of recurrent neural networks can overcome some of the limitations encountered in these early prototypes, and give an example involving the identification of anomalous events in a PWR 900 MW nuclear power plant.read more
Citations
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Bibliography of Self-Organizing Map SOM) Papers: 1998-2001 Addendum
TL;DR: This work has provided a keyword index to help finding articles of interest, and additionally a modern automatically constructed variant of a thematic index: a WEBSOM interface to the whole article collection of years 1981-2000.
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
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.
Journal ArticleDOI
Plant diagnostics by transient classification: The ALADDIN approach
TL;DR: Whereas a simple visual inspection of displayed trends is generally sufficient to allow the operator to confirm the plant status during normal, steady‐state operations, when the plant is subject to deviations due to anomalies or faults, the displayed trends of interacting variables can be very difficult to interpret, either because the changes are too subtle, or because theChanges are too fast.
Journal ArticleDOI
Fault diagnosis for heat pumps with parameter identification and clustering
TL;DR: In this article, a fault detection and diagnosis (FDD) system is presented based on a gray-box process model, the parameters of which are identified online using clustering methods.
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