Yellapu, V.S., Zhang, W., Vajpayee, V. orcid.org/0000-0003-1179-7118 et al. (1 more author) (2021) A multiscale data reconciliation approach for sensor fault detection. Progress in Nuclear Energy, 135. 103707. ISSN 0149-1970
Abstract
When used separately on the sensor data of the processes like nuclear reactors, the data reconciliation with fault detection and isolation strategy gives noise-corrupted estimates, and the wavelet transformation gives erroneous inferences about the operating point of the process under sensor fault conditions. Aiming to solve these challenging problems, a hybrid multi-scale data reconciliation scheme that combines data reconciliation with the wavelet transform is proposed in this work. The proposed method uses the steady-state data reconciliation framework under the assumption of consistent algebraic relationships among the wavelet coefficient data. The role of multivariate techniques in obtaining the algebraic relationships, online detection and isolation of sensor faults, orthogonal decomposition, and reconciliation of the wavelet coefficients data is demonstrated. It is shown that the reconciled estimates obtained from this method very closely represent the true behavior of the process as problems with respect to random noise, high-frequency components due to process faults, sensor faults, and the influence of sensor faults on the signal estimates are alleviated. The effectiveness of this method is quantitatively established when applied to the ex-core neutron detector data of the advanced heavy water reactor in various simulations.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 Elsevier Ltd. This is an author produced version of a paper subsequently published in Progress in Nuclear Energy. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Advanced heavy water reactor (AHWR); Data reconciliation; Ex-core neutron detectors; Fault detection and isolation (FDI); Ion chambers; Multiscale methods; Principal component analysis (PCA); Wavelets |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 05 Sep 2022 13:03 |
Last Modified: | 05 Sep 2022 13:03 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.pnucene.2021.103707 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:190548 |