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Dissimilarity Measures for ICA-Based Source Number Estimation

[+] Author Affiliations
Wei Cheng

Xi’an Jiaotong University, Xi’an, Shaanxi, ChinaUniversity of Michigan, Ann Arbor, MI

Seungchul Lee

University of Michigan, Ann Arbor, MI

Zhousuo Zhang, Zhengjia He

Xi’an Jiaotong University, Xi’an, Shaanxi, China

Paper No. MSEC2012-7340, pp. 683-688; 6 pages
doi:10.1115/MSEC2012-7340
From:
  • ASME 2012 International Manufacturing Science and Engineering Conference collocated with the 40th North American Manufacturing Research Conference and in participation with the International Conference on Tribology Materials and Processing
  • ASME 2012 International Manufacturing Science and Engineering Conference
  • Notre Dame, Indiana, USA, June 4–8, 2012
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 978-0-7918-5499-0
  • Copyright © 2012 by ASME

abstract

Most of blind source separation problems are carried out with a priori knowledge of the source numbers. However, for source separation-based machinery condition monitoring and fault diagnosis, it is a challenge work to determine the number of sources for a well source separation due to complex structures and nonlinear mixing mode. Therefore, source number estimation is a necessary and important procedure prior to source separation and further diagnosis work. In this paper, we focus on a novel source number estimation method based on independent component analysis (ICA) and clustering evaluation analysis, and investigate the performances of different dissimilarity measures of ICA-based source number estimations with typical mechanical vibration signals. Our work contributes to find an effective solution of source number estimation for source separation-based machinery condition monitoring and fault diagnosis.

Copyright © 2012 by ASME

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