@article {Yu:2017:0736-2935:422,
title = "Compressive sensing bases spinning mode detection for aeroengine noise",
journal = "INTER-NOISE and NOISE-CON Congress and Conference Proceedings",
parent_itemid = "infobike://ince/incecp",
publishercode ="ince",
year = "2017",
volume = "255",
number = "7",
publication date ="2017-12-07T00:00:00",
pages = "422-433",
itemtype = "ARTICLE",
issn = "0736-2935",
url = "https://ince.publisher.ingentaconnect.com/content/ince/incecp/2017/00000255/00000007/art00049",
author = "Yu, Wenjun and Ma, Zhengyu and Huang, Xun",
abstract = "This paper presents a compressive sensing based experimental method, which can detect sparse spinning modes of sound waves inside an aeroengine duct system. Considering the fact that the conventional mode detection method, which is based on Shannon-Nyquist sampling theorem, is oversampled
for modern turbofan engine system, the compressive sensing theory is adopted to decrease the spatial sampling points, which can save the costs of data acquisition, signal processing and data storage in experiments. In our previous work, sparse azimuthal modes can be detected accurately by
the compressive sensing method with much less sensors than conventional methods. Here we extend the compressive sensing method to conduct radial mode detection with azimuthal mode detection simultaneously. The proposed new method is validated by amount of numerical simulations and circular
array experiments. Both the numerical simulations and the experiments results are satisfactory, even when the practical issue of the background noise pollution is taken into account (SNR=15dB). Algorithm stability, robustness and modes coherence are also discussed to estimate its performance
in various working condition. The proposed method can also improve the mode order detection range and is beneficial for sensory array tests of silent aeroengines in particular and some other engineering systems with duct acoustics in general.",
}