Demonstration of an improved passive acoustic fault detection method on recordings from the Phénix steam generator operating at full power. (March 2017)
- Record Type:
- Journal Article
- Title:
- Demonstration of an improved passive acoustic fault detection method on recordings from the Phénix steam generator operating at full power. (March 2017)
- Main Title:
- Demonstration of an improved passive acoustic fault detection method on recordings from the Phénix steam generator operating at full power
- Authors:
- Riber Marklund, Anders
Michel, Frédéric
Anglart, Henryk - Abstract:
- Highlights: An improved feature calculation for the hidden Markov model method for passive acoustic leak detection is presented. The method uses background noise learning only, i.e. is independent of leak noise characteristics. A new normalization leads to important reduction of training data needed. An exhaustive model parameter study is included. The method is demonstrated on acoustic noise from the Phénix steam generator operating at full power. Abstract: A hidden Markov model method proposed earlier for passive acoustic leak detection in sodium fast reactor systems has been improved in order to clarify how to set all free model parameters and to allow smaller amounts of training data. The method is based on training the model on known background noise only and optimizing its free model parameters by a parametric study of detection performance for synthetic noises superposed onto the same background. This means that the method is not assuming any knowledge on the noise to be detected and may be used as a general fault detection method, even if the application envisaged here is leak detection for sodium fast reactors. Using recordings of background noise as well as from argon injection tests performed at full power in the Phénix sodium fast reactor plant, it is estimated that the resulting method will detect leak-like deviations from the background noise with a detection delay of a few seconds, a false alarm rate close to 10 - 8 per second and at signal-to-noise ratioHighlights: An improved feature calculation for the hidden Markov model method for passive acoustic leak detection is presented. The method uses background noise learning only, i.e. is independent of leak noise characteristics. A new normalization leads to important reduction of training data needed. An exhaustive model parameter study is included. The method is demonstrated on acoustic noise from the Phénix steam generator operating at full power. Abstract: A hidden Markov model method proposed earlier for passive acoustic leak detection in sodium fast reactor systems has been improved in order to clarify how to set all free model parameters and to allow smaller amounts of training data. The method is based on training the model on known background noise only and optimizing its free model parameters by a parametric study of detection performance for synthetic noises superposed onto the same background. This means that the method is not assuming any knowledge on the noise to be detected and may be used as a general fault detection method, even if the application envisaged here is leak detection for sodium fast reactors. Using recordings of background noise as well as from argon injection tests performed at full power in the Phénix sodium fast reactor plant, it is estimated that the resulting method will detect leak-like deviations from the background noise with a detection delay of a few seconds, a false alarm rate close to 10 - 8 per second and at signal-to-noise ratio conditions at least corresponding to an additive signal at −10 dB. The method is one-channel, i.e. using input from one single acoustic sensor only. … (more)
- Is Part Of:
- Annals of nuclear energy. Volume 101(2017:Mar.)
- Journal:
- Annals of nuclear energy
- Issue:
- Volume 101(2017:Mar.)
- Issue Display:
- Volume 101 (2017)
- Year:
- 2017
- Volume:
- 101
- Issue Sort Value:
- 2017-0101-0000-0000
- Page Start:
- 1
- Page End:
- 14
- Publication Date:
- 2017-03
- Subjects:
- Sodium fast reactors -- Acoustic leak detection -- Fault detection algorithms -- Hidden Markov models
Nuclear energy -- Periodicals
Nuclear engineering -- Periodicals
621.4805 - Journal URLs:
- http://www.sciencedirect.com/science/journal/03064549 ↗
http://catalog.hathitrust.org/api/volumes/oclc/2243298.html ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.anucene.2016.10.003 ↗
- Languages:
- English
- ISSNs:
- 0306-4549
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 1043.150000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 744.xml