Conference paper Open Access

General Regression Neural Networks for the Concurrent, Timely and Reliable Identification of Detector Malfunctions and/or Nuclear Reactor Deviations from Steady-State Operation

Tatiana Tambouratzis; Dionysios Chionis; Abdelhamid Dokhane

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<identifier identifierType="DOI">10.5281/zenodo.3047075</identifier>
<creators>
<creator>
<creatorName>Tatiana Tambouratzis</creatorName>
<affiliation>University of Piraeus</affiliation>
</creator>
<creator>
<creatorName>Dionysios Chionis</creatorName>
<affiliation>Paul Scherrer Institute</affiliation>
</creator>
<creator>
<creatorName>Abdelhamid Dokhane</creatorName>
<affiliation>Paul Scherrer Institute</affiliation>
</creator>
</creators>
<titles>
<title>General Regression Neural Networks for the Concurrent, Timely and Reliable Identification of Detector Malfunctions and/or Nuclear Reactor Deviations from Steady-State Operation</title>
</titles>
<publisher>Zenodo</publisher>
<publicationYear>2018</publicationYear>
<subjects>
<subject>The analysis and understanding of the neutron flux (NF) signals of nuclear reactors (NRs) is imperative for ensuring safe and optimal (expressed in terms of minimal fuel use for maximal energy production) on-line NR operation. The NF perturbations are of particular interest, as they provide detailed information concerning the instantaneous changes in NR operation/status. In this piece of research, general regression artificial neural networks (GRNNs) are proposed for concurrently identifying NR deviations from steady-state operation as well as neutron detector (ND) malfunctions in a timely, reliable and efficient manner. On the one hand, the use of (a) raw, minimalistic NF signals and (b) complementary signal encodings – derived from pertinent and limited in size ND configurations – of the problem space, renders the proposed approach timely/efficient, modular as well as flexible. On the other hand, the GRNN characteristics of (i) transparency of construction, (ii) low computational (time/space) complexity of training and testing, (iii) accuracy, consistency and good generalization in the identification of the cause(s) behind deviating-from-normal NR behaviour and (iv) efficient operation and partial only GRNN retraining following modification of the training set, support the use of the proposed methodology. It is envisaged that, by appropriately combining the responses derived from different GRNNs, both accuracy and sensitivity of deviation detection as well as of malfunction localization shall be further improved at minimal additional computational load</subject>
</subjects>
<dates>
<date dateType="Issued">2018-11-18</date>
</dates>
<language>en</language>
<resourceType resourceTypeGeneral="ConferencePaper"/>
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<alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3047075</alternateIdentifier>
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<relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3047074</relatedIdentifier>
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<rightsList>
<rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
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<descriptions>
<description descriptionType="Abstract">&lt;p&gt;The analysis and understanding of the neutron flux (NF) signals of nuclear reactors (NRs) is imperative for ensuring safe and optimal (expressed in terms of minimal fuel use for maximal energy production) on-line NR operation. The NF perturbations are of particular interest, as they provide detailed information concerning the instantaneous changes in NR operation/status. In this piece of research, general regression artificial neural networks (GRNNs) are proposed for concurrently identifying NR deviations from steady-state operation as well as neutron detector (ND) malfunctions in a timely, reliable and efficient manner. On the one hand, the use of (a) raw, minimalistic NF signals and (b) complementary signal encodings &amp;ndash; derived from pertinent and limited in size ND configurations &amp;ndash; of the problem space, renders the proposed approach timely/efficient, modular as well as flexible. On the other hand, the GRNN characteristics of (i) transparency of construction, (ii) low computational (time/space) complexity of training and testing, (iii) accuracy, consistency and good generalization in the identification of the cause(s) behind deviating-from-normal NR behaviour and (iv) efficient operation and partial only GRNN retraining following modification of the training set, support the use of the proposed methodology. It is envisaged that, by appropriately combining the responses derived from different GRNNs, both accuracy and sensitivity of deviation detection as well as of malfunction localization shall be further improved at minimal additional computational load.&lt;/p&gt;</description>
</descriptions>
<fundingReferences>
<fundingReference>
<funderName>European Commission</funderName>
<funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
<awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/754316/">754316</awardNumber>
<awardTitle>Core monitoring techniques and experimental validation and demonstration</awardTitle>
</fundingReference>
</fundingReferences>
</resource>

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