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

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Source Contribution Evaluation of Mechanical Vibration Signals via Enhanced Independent Component Analysis

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Title
Source Contribution Evaluation of Mechanical Vibration Signals via Enhanced Independent Component Analysis
Author
Cheng, WeiZhang, ZhousuoLee, SeungchulHe, Zhengjia
Keywords
blind source separation; clustering evaluation; independent component analysis; source contribution
Issue Date
2012-04
Publisher
ASME-AMER SOC MECHANICAL ENG
Citation
JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THEASME, v.134, no.2, pp.1 - 9
Abstract
Extraction of effective information from measured vibration signals is a fundamental task for the machinery condition monitoring and fault diagnosis. As a typical blind source separation (BSS) method, independent component analysis (ICA) is known to be able to effectively extract the latent information in complex signals even when the mixing mode and sources are unknown. In this paper, we propose a novel approach to overcome two major drawbacks of the traditional ICA algorithm: lack of robustness and source contribution evaluation. The enhanced ICA algorithm is established to escalate the separation performance and robustness of ICA algorithm. This algorithm repeatedly separates the mixed signals multiple times with different initial parameters and evaluates the optimal separated components by the clustering evaluation method. Furthermore, the source contributions to the mixed signals can also be evaluated. The effectiveness of the proposed method is validated through the numerical simulation and experiment studies.
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DOI
10.1115/1.4005806
ISSN
1087-1357
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DHE_Journal Papers
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