Microphone array analysis for simultaneous condition detection, localization, and classification in a pipe.
| Autor: | Yu Y; Department of Mechanical Engineering, University of Sheffield, Mappin Street, Sheffield S1 3JD, United Kingdom., Worley R; Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield S1 3JD, United Kingdom., Anderson S; Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield S1 3JD, United Kingdom., Horoshenkov KV; Department of Mechanical Engineering, University of Sheffield, Mappin Street, Sheffield S1 3JD, United Kingdom. |
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| Jazyk: | angličtina |
| Zdroj: | The Journal of the Acoustical Society of America [J Acoust Soc Am] 2023 Jan; Vol. 153 (1), pp. 367. |
| DOI: | 10.1121/10.0016856 |
| Abstrakt: | An acoustic method for simultaneous condition detection, localization, and classification in air-filled pipes is proposed. The contribution of this work is threefold: (1) a microphone array is used to extend the usable acoustic frequency range to estimate the reflection coefficient from blockages and lateral connections; (2) a robust regularization method of sparse representation based on a wavelet basis function is adapted to reduce the background noise in acoustical data; and (3) the wavelet components are used to localize and classify the condition of the pipe. The microphone array and sparse representation method enhance the acoustical signal reflected from blockages and lateral connections and suppress unwanted higher-order modes. Based on the sparse representation results, higher-level wavelet functions representing the impulse response are used to localize the position of the sensor corresponding to a blockage or lateral connection with higher spatial resolution. It is shown that the wavelet components can be used to train and to test a support vector machine (SVM) classifier for the condition identification more accurately than with a time domain SVM classifier. This work paves the way for the development of simultaneous condition classification and localization methods to be deployed on autonomous robots working in buried pipes. |
| Databáze: | MEDLINE |
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