An Adaptive Classifier for Detecting Helicopter Drivetrain Damage Using Acoustic Emission

  • Mark Friesel
  • Yuyin Ji
  • Wu Yan
  • Ron Miller
  • Mark Carlos
Chapter
Part of the Review of Progress in Quantitative Nondestructive Evaluation book series (RPQN, volume 18 A)

Abstract

This paper describes recent developments in a program to detect damage to helicopter drivetrains using acoustic emission (AE)1,2. Data obtained from an SH-60 drivetrain on an NAWC test stand was correlated with seeded fault damage in order to identify acoustic emission characteristics unique to the various sources. The objective is to extend prior work in applications of pattern recognition techniques and advanced machine intelligence to AE3,4,5 by designing and implementing an autonomous adaptive procedure to recognize and classify drivetrain damage from AE data.

Keywords

Acoustics 

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References

  1. 1.
    Siores, E. and A. A. Negro (Feb. 1997), ‘Condition monitoring of a gear box using acoustic emission’, Mat. Eval. p. 1Google Scholar
  2. 2.
    Almeida, A.F., W. D. Martin, D. J. Pointer (1997), ‘Application of acoustic emission to health monitoring of helicopter mechanical system’, proc. FAA-NASA Symposium on the continued airworthiness of aircraft structures’, avail, through the National Technical Information Service, Springfield VA, p.89Google Scholar
  3. 3.
    Friesel, M. A. (July 1989), ‘An application of signal analysis techniques to acoustic emission from a cyclically loaded aluminum joint specimen’, Mat. Eval., no. 7, p. 842Google Scholar
  4. 4.
    Barga, R.S., M.A. Friesel, R. B. Melton (April 1990), SPIE Technical Symposium, Orlando, FLGoogle Scholar
  5. 5.
    Walker, J. L., S. S. Russell, G. L. Workman, E. v. K. Hill (Aug. 1997), ‘Neural network/acoustic emission burst pressure prediction for impact damaged composite pressure vessels’, Mat. Eval., p. 903Google Scholar

Copyright information

© Springer Science+Business Media New York 1999

Authors and Affiliations

  • Mark Friesel
    • 1
  • Yuyin Ji
    • 1
  • Wu Yan
    • 1
  • Ron Miller
    • 1
  • Mark Carlos
    • 1
  1. 1.Physical Acoustics CorporationPrincetonUSA

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