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Conference papers
Passive Acoustic Leak Detection for Sodium Cooled Fast Reactors Using Hidden Markov Models
Abstract : 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.
Cited literature [11 references]
https://hal-cea.archives-ouvertes.fr/cea-02509266
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Submitted on : Monday, March 16, 2020 - 3:52:02 PM
Last modification on : Tuesday, April 28, 2020 - 11:28:18 AM
Long-term archiving on: : Wednesday, June 17, 2020 - 3:19:49 PM
Contributor : Amplexor Amplexor Connect in order to contact the contributor
Submitted on : Monday, March 16, 2020 - 3:52:02 PM
Last modification on : Tuesday, April 28, 2020 - 11:28:18 AM
Long-term archiving on: : Wednesday, June 17, 2020 - 3:19:49 PM
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