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Lee, Seungchul
iSystems Design Lab
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  • Intelligent design for products and manufacturing systems

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Independent component analysis based source number estimation and its comparison for mechanical systems

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Title
Independent component analysis based source number estimation and its comparison for mechanical systems
Author
Cheng, WeiLee, SeungchulZhang, ZhousuoHe, Zhengjia
Keywords
Akaike information criterion; Bayesian information criterion; Clustering evaluation; Dissimilarity measures; Mechanical signals; Mechanical systems; Minimum description length; Mixed signal; Source number estimation
Issue Date
2012-11
Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
Citation
JOURNAL OF SOUND AND VIBRATION, v.331, no.23, pp.5153 - 5167
Abstract
It has been challenging to correctly separate the mixed signals into source components when the source number is not known a priori. In this paper, we propose a novel source number estimation based on independent component analysis (ICA) and clustering evaluation analysis. We investigate and benchmark three information based source number estimations: Akaike information criterion (AIC), minimum description length (MDL) and improved Bayesian information criterion (IBIC). All the above methods are comparatively studied in both numerical and experimental case studies with typical mechanical signals. The results demonstrate that the proposed. ICA based source number estimation with nonlinear dissimilarity measures performs more stable and robust than the information based ones for mechanical systems.
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DOI
10.1016/j.jsv.2012.06.021
ISSN
0022-460X
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DHE_Journal Papers
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