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A Nonlinear Time Series Analysis Method for Health Monitoring of Rolling Bearings

[+] Author Affiliations
Yongbin Liu

University of Science and Technology of China, Hefei, Anhui, China

Ruqiang Yan

Southeast University, Nanjing, Jiangsu, China

Robert X. Gao

University of Connecticut, Storrs, CT

Paper No. DSCC2010-4118, pp. 277-283; 7 pages
doi:10.1115/DSCC2010-4118
From:
  • ASME 2010 Dynamic Systems and Control Conference
  • ASME 2010 Dynamic Systems and Control Conference, Volume 2
  • Cambridge, Massachusetts, USA, September 12–15, 2010
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-4418-2 | eISBN: 978-0-7918-3884-6
  • Copyright © 2010 by ASME

abstract

This paper presents a nonlinear time series analysis method for rotating machine damage detection and diagnostics. Specifically, the permutation entropy is investigated as a statistical measure for signal characterization. Through space reconstruction, the permutation entropy describes the complexity of the time series measured on a physical system, and takes its non-linear behavior into account. By identifying changes in the vibration signals measured on rotating machines, which are typical precursors of defect occurrence, permutation entropy can serve as a diagnostic tool. Experiments on a custom-designed gearbox system have confirmed its effectiveness for machine structural health monitoring applications.

Copyright © 2010 by ASME

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