Mode extraction from acoustic backscatter in a waveguide

2005 ◽  
Vol 117 (4) ◽  
pp. 2612-2612
Author(s):  
Shane Walker ◽  
Philippe Roux ◽  
William A. Kuperman
Keyword(s):  
Acoustic Backscatter ◽  
Mode Extraction

Bathymetry and Acoustic Backscatter: Northern Santa Barbara Channel, Southern California

2010 ◽  
Author(s):  
Pete Dartnell ◽  
David Finlayson ◽  
Jamie Conrad ◽  
Guy Cochrane ◽  
Samuel Johnson
Keyword(s):  
Southern California ◽  
Acoustic Backscatter ◽  
Santa Barbara Channel ◽  
Santa Barbara

Roughness Measurements of Soil Surfaces by Acoustic Backscatter

2003 ◽  
Vol 67 (1) ◽  
pp. 241 ◽  
Author(s):  
Michael L. Oelze ◽  
James M. Sabatier ◽  
Richard Raspet
Keyword(s):  
Acoustic Backscatter ◽  
Roughness Measurements

scholarly journals Getting information on suspended sediments in a large river from acoustic backscatter

2018 ◽  
Vol 40 ◽  
pp. 04017
Author(s):  
Adrien Vergne ◽  
Céline Berni ◽  
Jérôme Le Coz
Keyword(s):  
Suspended Sediment Concentration ◽  
Spatial Information ◽  
Suspended Sediments ◽  
Sediment Concentration ◽  
Large River ◽  
Acoustic Backscatter ◽  
Qualitative Information ◽  
Sediment Size ◽  
The Common

There has been a growing interest in the last decade in extracting information on Suspended Sediment Concentration (SSC) from acoustic backscatter in rivers. Quantitative techniques are not yet effective, but acoustic backscatter already provides qualitative information on suspended sediments. In particular, in the common case of a bi-modal sediment size distribution, corrected acoustic backscatter can be used to look for sand particles in suspension and provide spatial information on their distribution throughout a river crosssection. This paper presents a case-study where these techniques have been applied.

Diel, lunar and seasonal vertical migration in the deep western Gulf of Mexico evidenced from a long-term data series of acoustic backscatter

2021 ◽  
pp. 102562
Author(s):  
Laura Ursella ◽  
Sara Pensieri ◽  
Enric Pallàs-Sanz ◽  
Sharon Z. Herzka ◽  
Roberto Bozzano ◽  
...  
Keyword(s):  
Gulf Of Mexico ◽  
Vertical Migration ◽  
Acoustic Backscatter ◽  
Data Series ◽  
Term Data

A new fault diagnosis method based on adaptive spectrum mode extraction

2021 ◽  
pp. 147592172098694
Author(s):  
Zhijian Wang ◽  
Ningning Yang ◽  
Naipeng Li ◽  
Wenhua Du ◽  
Junyuan Wang
Keyword(s):  
Scale Space ◽  
Variational Mode Decomposition ◽  
Rolling Bearings ◽  
Mode Decomposition ◽  
Penalty Factor ◽  
Diagnosis Method ◽  
Mode Extraction ◽  
Non Stationary Signal ◽  
Envelope Spectrum ◽  
Fault Feature Extraction

Variational mode decomposition provides a feasible method for non-stationary signal analysis, but the method is still not adaptive, which greatly limits the wide application of the method. Therefore, an adaptive spectrum mode extraction method is proposed in this article. The proposed method is mainly composed of spectral segmentation, mode extraction, and feedback adjustment. In the spectral segmentation part, considering the lack of robustness of classical scale space in a strong noise environment, this article proposes a method of fault feature mapping, which solves over-decomposition of variational mode decomposition guided by classical scale space. In the mode extraction part, for insufficient self-adaptability of the single penalty factor in the variational mode decomposition method, this article proposes a method of spectral aggregation factor, which solves multiple penalty factors that conform to different intrinsic modal functions. In the feedback adjustment part, this article proposes the method of transboundary criterion, which makes the result of variational mode decomposition within a preset range. Finally, using dispersion entropy and kurtosis indicators, intrinsic modal function components containing fault frequencies are extracted for envelope spectrum analysis to extract fault characteristic frequencies. In order to verify the effectiveness of the proposed method, the proposed method is applied to simulation signals and bearing fault signals. Comparing the decomposition results of different methods, the conclusion shows that the proposed method is more advantageous for the fault feature extraction of rolling bearings.

An optimized variational mode extraction method for rolling bearing fault diagnosis

2021 ◽  
pp. 147592172110066
Author(s):  
Bin Pang ◽  
Mojtaba Nazari ◽  
Zhenduo Sun ◽  
Jiaying Li ◽  
Guiji Tang
Keyword(s):  
Extraction Method ◽  
Fitness Function ◽  
Rolling Bearing ◽  
Initial Guess ◽  
Center Frequency ◽  
Variational Mode Decomposition ◽  
Bearing Fault ◽  
Mode Decomposition ◽  
Two Parameters ◽  
Mode Extraction

The fault feature signal of rolling bearing can be characterized as the narrow-band signal with a specific resonance frequency. Therefore, resonance demodulation analysis is a powerful damage detection technique of bearings. In addition to the fault feature signal, the measured vibration signals carry various interference components, and these interference components become a serious obstacle of fault feature extraction. Variational mode extraction is a novel signal analysis method designed to retrieve a specific signal component from the composite signal. Variational mode extraction is founded on a similar basis as variational mode decomposition, while it shows better accuracy and higher efficiency compared with variational mode decomposition. In this study, variational mode extraction is introduced to the resonance demodulation analysis of bearing fault. As the results of variational mode extraction analysis are greatly influenced by the choice of two parameters, that is, the balancing factor α and the initial guess of center frequency ωd, an optimized variational mode extraction method is further developed. First, a new fault information evaluation index for measuring the richness of fault characteristics of the signal, termed ensemble impulsiveness and cyclostationarity, is formulated. Second, the ensemble impulsiveness and cyclostationarity is used as the fitness function of particle swarm optimization to automatically determine the optimal values of α and ωd. Finally, the validity of optimized variational mode extraction method is verified by simulated and experimental analysis, and the superiority of optimized variational mode extraction method is highlighted through comparison with two other advanced resonance demodulation analysis approaches, that is, the improved kurtogram and infogram. The analysis results indicate that optimized variational mode extraction method has a powerful capability of resonance demodulation analysis.

scholarly journals Acoustic backscatter at a Red Sea whale shark aggregation site

2018 ◽  
Vol 20 ◽  
pp. 23-33 ◽  
Author(s):  
Aya Hozumi ◽  
Stein Kaartvedt ◽  
Anders Røstad ◽  
Michael L. Berumen ◽  
Jesse E.M. Cochran ◽  
...  
Keyword(s):  
Red Sea ◽  
Acoustic Backscatter ◽  
Whale Shark ◽  
Aggregation Site
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