Abstract
To identify sound source locations by using Fourier-based Near-field Acoustical Holography (NAH), a large number of microphone measurements is generally required to span the source region and ensure a sufficiently high spatial sampling rate. As a result, such measurements are costly, a fact which has discouraged the industrial application of NAH to identify sound source locations. However, recently, compressive sensing approaches have made it feasible to identify concentrated sound sources with a limited number of microphone measurements. In the present work, sound radiation from the front face of a diesel engine was measured by using one set of measurements from a 35-channel combo-array. The locations of significant noise sources were then identified by using three compressive sensing algorithms: Wideband Acoustical Holography (WBH), l1-norm minimization, and a hybrid approach which combined WBH and l1-norm minimization. The latter approach takes advantage of the l1’ norm’s ability to locate spatially distinct sources, and WBH’s ability to suppress “ghost” sources. It was found that the hybrid algorithm can localize and visualize the major noise sources over a broad range of frequencies, even though using a relatively small number of microphones. Finally, comments are made regarding sound field reconstruction differences between the algorithms.
Keywords
Nearfield acoustical holography, Compressive sensing, Equivalent source method, Wideband acoustic holography, L1 norm, Diesel engine, Source identification
Subject
Acoustics and Noise Control
Date of this Version
5-8-2023
Comments
Tongyang Shi and J. Stuart Bolton, “A hybrid compressive sensing approach to noise source visualization: Application to a diesel engine,” 184th meeting of the Acoustical Society of America, Chicago IL, May 2023. Paper 1aSP6. Abstract published in the Journal of the Acoustical Society of America, Vol. 153(3) Pt. 2, A55, 2023. DOI:https://doi.org/10.1121/10.0018144