@article {Shi:2023:0736-2935:1990,
title = "Diesel Engine Noise Source Visualization by Using Compressive Sensing Algorithms",
journal = "INTER-NOISE and NOISE-CON Congress and Conference Proceedings",
parent_itemid = "infobike://ince/incecp",
publishercode ="ince",
year = "2023",
volume = "265",
number = "6",
publication date ="2023-02-01T00:00:00",
pages = "1990-1999",
itemtype = "ARTICLE",
issn = "0736-2935",
url = "https://ince.publisher.ingentaconnect.com/content/ince/incecp/2023/00000265/00000006/art00114",
doi = "doi:10.3397/IN_2022_0284",
author = "Shi, Tongyang and Bolton, J. Stuart and Eberhardt, Frank",
abstract = "To identify sound source locations by using Near-field Acoustical Holography (NAH), a large number of microphone measurements is generally required in order to cover the source region and ensure a sufficiently high spatial sampling rate: it may require hundreds of microphones. As a
result, such measurements are costly, a fact which has limited the industrial application of NAH to identify sound source locations. However, recently, it has been shown possible to identify concentrated sound sources with a limited number of microphone measurement based on Compressive Sensing
theory. In the present work, sound radiation from the front face of a diesel engine was measured by using one set of measurements from a thirty-five-channel combo-array placed in front of the engine. The locations of significant noise sources were then identified by using two algorithms: i.e.,
l1-norm minimization and a hybrid approach which combined Wideband Acoustical Holography (WBH) and l1-norm minimization. It was found that both algorithms can successfully 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 on sound field reconstruction differences between the two algorithms.",
}