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Research Papers

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

[+] Author and Article Information
Wei Cheng

 State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi Prov., P.R. China 710049; Department of Mechanical Engineering,  S. M. Wu Manufacturing Research Center,Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109,wche@umich.edu

Zhousuo Zhang1

 State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an,Shaanxi Prov., P.R. China 710049zzs@mail.xjtu.edu.cn

Seungchul Lee

S. M. Wu Manufacturing Research Center,  Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109seunglee@umich.edu

Zhengjia He

 State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi Prov., P.R. China 710049hzj@mail.xjtu.edu.cn

1

Corresponding author.

J. Manuf. Sci. Eng 134(2), 021014 (Apr 04, 2012) (9 pages) doi:10.1115/1.4005806 History: Revised November 03, 2005; Received May 02, 2011; Published March 30, 2012; Online April 04, 2012

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.

Copyright © 2012 by American Society of Mechanical Engineers
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Figures

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Figure 1

Framework of the fast fixed point algorithm

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Figure 2

Waveforms of the source signals

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Waveforms of the mixed signals

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Figure 4

By the fast fixed point algorithm

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By the enhanced ICA algorithm

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Reliable evaluation of the enhanced ICA algorithm

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Waveforms of the source signals

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Linearly mixed signals

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Reconstructed waveforms of the independent components

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ICs extracted from the mixed signals

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Figure 14

The structural diagram of Motor 1

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The structural diagram of Motor 2

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Source signals near motors

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The test-bed and experiment equipment

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The structural diagram of the test-bed

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Figure 12

Mixed vibration signals measured on the shell

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