Acoustical gas-leak detection in the presence of multiple reflections, dispersion, and uncorrelated noise using optimized residual complexity
Precise acoustical leak detection calls for robust time-delay estimates, which minimize the probability of false alarms in the face of dispersive propagation, multiple reflections, and uncorrelated background noise. Providing evidence that higher order modes and multi-reflected signals behave like s...
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Published in: | The Journal of the Acoustical Society of America Vol. 140; no. 3; pp. 1817 - 1827 |
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Main Authors: | , , , , |
Format: | Journal Article |
Language: | English |
Published: |
United States
01-09-2016
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Online Access: | Get full text |
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Summary: | Precise acoustical leak detection calls for robust time-delay estimates, which minimize the probability of false alarms in the face of dispersive propagation, multiple reflections, and uncorrelated background noise. Providing evidence that higher order modes and multi-reflected signals behave like sets of correlated noise, this work uses a regression model to optimize residual complexity in the presence of both correlated and uncorrelated noise. This optimized residual complexity (ORC) is highly robust since it takes into account both the level and complexity of noise. The lower complexity of the dispersive modes and multiple reflections, compared to the complexity of the plane mode, points to the robustness of ORC against multiple reflections and dispersion. Experimental investigations using recorded sounds of gas leaking from a pipe confirm the robustness of ORC against multiple reflections. Numerical simulations also show robustness against dispersive modes, even when they disturb the linearity of the cross-spectrum phase. Comparisons with other methods—mutual information, cross correlation, and residual complexity—underline the general advantages of ORC in terms of robustness in the presence of reflection and dispersion, against both correlated and uncorrelated noise, and to short signals. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0001-4966 1520-8524 |
DOI: | 10.1121/1.4962482 |