Seismically Detecting Nuclear Reactor Operations Using a Power Spectral Density (PSD) Misfit Detector

2021 ◽  
Author(s):  
David L. Guenaga ◽  
Chengping Chai ◽  
Monica Maceira ◽  
Omar E. Marcillo ◽  
Aaron A. Velasco
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Nuclear Reactor ◽  
National Laboratory ◽  
Waveform Data ◽  
Oak Ridge ◽  
Power Spectral ◽  
And Performance ◽  
Additional Station ◽  
Initial Results

ABSTRACT To explore the ability to indirectly detect and attribute various operations conducted at a nuclear reactor using waveform data, we investigated the seismic signals recorded near the High Flux Isotope Reactor (HFIR) located at Oak Ridge National Laboratory in Oak Ridge, Tennessee. Specifically, we processed seismic data collected from a single seismoacoustic station, WACO, near the HFIR facility, and employed a power spectral density misfit detector to identify signals of interest and associate the detections with operational events. Initial results suggest that this method provides a promising means of regularly detecting at least 19 unique operations. With additional station deployment and more comprehensive data logs, we anticipate that future analysis will offer an additional means to seismically monitor nuclear reactors (such as HFIR) health and performance more accurately.

Fuzzy Modeling of Multi-Degree-of-Freedom Power Spectral Density Systems

2009 ◽  
Vol 2 (1) ◽  
pp. 40-47
Author(s):  
Montasser Tahat ◽  
Hussien Al-Wedyan ◽  
Kudret Demirli ◽  
Saad Mutasher
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Fuzzy Modeling ◽  
Degree Of Freedom ◽  
Power Spectral

scholarly journals Noise power spectral density scaled SNR response estimation with restricted range search for sound source localisation using unmanned aerial vehicles

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Benjamin Yen ◽  
Yusuke Hioka
Keyword(s):  
Spectral Density ◽  
Noise Reduction ◽  
Power Spectral Density ◽  
Sound Source ◽  
Processing Algorithm ◽  
Post Processing ◽  
Time Frequency ◽  
Distance Error ◽  
Rotor Noise ◽  
Power Spectral

Abstract A method to locate sound sources using an audio recording system mounted on an unmanned aerial vehicle (UAV) is proposed. The method introduces extension algorithms to apply on top of a baseline approach, which performs localisation by estimating the peak signal-to-noise ratio (SNR) response in the time-frequency and angular spectra with the time difference of arrival information. The proposed extensions include a noise reduction and a post-processing algorithm to address the challenges in a UAV setting. The noise reduction algorithm reduces influences of UAV rotor noise on localisation performance, by scaling the SNR response using power spectral density of the UAV rotor noise, estimated using a denoising autoencoder. For the source tracking problem, an angular spectral range restricted peak search and link post-processing algorithm is also proposed to filter out incorrect location estimates along the localisation path. Experimental results show the proposed extensions yielded improvements in locating the target sound source correctly, with a 0.0064–0.175 decrease in mean haversine distance error across various UAV operating scenarios. The proposed method also shows a reduction in unexpected location estimations, with a 0.0037–0.185 decrease in the 0.75 quartile haversine distance error.

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