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Passive acoustic leak detection for sodium fast reactors using hidden Markov models
KTH, School of Engineering Sciences (SCI), Physics, Reactor Technology. Commisariat Energie Atom & Energies Alternat CEA, France.ORCID iD: 0000-0003-1358-9355
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2016 (English)In: IEEE Transactions on Nuclear Science, ISSN 0018-9499, E-ISSN 1558-1578Article in journal (Refereed) Published
Abstract [en]

Acoustic leak detection for steam generators of sodium fast reactors have been an active research topic since the early 1970s and several methods have been tested over the years. Inspired by its success in the field of automatic speech recognition, we here apply hidden Markov models (HMM) in combination with Gaussian mixture models (GMM) to the problem. To achieve this, we propose a new feature calculation scheme, based on the temporal evolution of the power spectral density (PSD) of the signal. Using acoustic signals recorded during steam/water injection experiments done at the Indira Gandhi Centre for Atomic Research (IGCAR), the proposed method is tested. We perform parametric studies on the HMM+GMM model size and demonstrate that the proposed method a) performs well without a priori knowledge of injection noise, b) can incorporate several noise models and c) has an output distribution that simplifies false alarm rate control.

Place, publisher, year, edition, pages
IEEE , 2016.
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-183465DOI: 10.1109/TNS.2015.2502400ISI: 000379928300005OAI: oai:DiVA.org:kth-183465DiVA: diva2:911525
Funder
Swedish Research Council, B0774801
Note

QC 20160329

Available from: 2016-03-13 Created: 2016-03-13 Last updated: 2016-08-12Bibliographically approved

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Publisher's full texthttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7414513&newsearch=true&queryText=passive%20acoustic%20leak%20detection%20hidden%20markov%20models

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CiteExportLink to record
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