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A Novel Blind Deconvolution De-Noising Scheme in Failure Prognosis

  • Bing Zhang
  • Taimoor Khawaja
  • Romano Patrick
  • George Vachtsevanos
  • Marcos Orchard
  • Abhinav Saxena
Chapter
Part of the Intelligent Systems, Control, and Automation: Science and Engineering book series (ISCA, volume 39)

With increased system complexity, Condition-Based Maintenance (CBM) becomes a promising solution to system safety by detecting faults and scheduling maintenance procedures before faults become severe failures resulting in catastrophic events. For CBM of many mechanical systems, fault diagnosis and failure prognosis based on vibration signal analysis are essential techniques. Noise originating from various sources, however, often corrupts vibration signals and degrades the performance of diagnostic and prognostic routines, and consequently, the performance of CBM. In this paper, a new de-noising structure is proposed and applied to vibration signals collected from a testbed of the main gearbox of a helicopter subjected to a seeded fault. The proposed structure integrates a blind deconvolution algorithm, feature extraction, failure prognosis, and vibration modeling into a syn-ergistic system, in which the blind deconvolution algorithm attempts to arrive at the true vibration signal through an iterative optimization process. Performance indexes associated with quality of the extracted features and failure prognosis are addressed, before and after de-noising, for validation purposes.

Keywords

Fault Diagnosis Vibration Signal Blind Source Separation Planetary Gear Blind Deconvolution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    G. Vachtsevanos, F. Lewis, M. Roemer, A. Hess and B. Wu, Intelligent Fault Diagnosis and Prognosis for Engineering Systems, Wiley, 2006.Google Scholar
  2. 2.
    A. Saxena, B. Wu and G. Vachtsevanos, A methodology for analyzing vibration data from planetary gear system using complex morelt wavelets, in Proceedings of American Control Conference, Portland, OR, June, Vol. 7, pp. 4730–4735, 2005.Google Scholar
  3. 3.
    B. Wu, A. Saxena, T. Khawaja, R. Patrick, G. Vachtsevanos and R. Sparis, An approach to fault diagnosis of helicopter planetary gears, in Proceedings of IEEE AUTOTESTCON, San Antonio, TX, September pp. 475–481, 2004.Google Scholar
  4. 4.
    B. Wu, R.P.A. Saxena and G. Vachtsevanos, Vibration monitoring for fault diagnosis of helicopter planetary gears, in Proceedings of IFAC World Congress, Prague, Czech Republic, July 2005.Google Scholar
  5. 5.
    M. Lebold, K. McClintic, R. Campbell, C. Byington and K. Maynard, Review of vibration analysis methods for gearbox diagnostics and prognostics, in Proceedings of 54th Meeting of the Society for Machineary Failure Prevention Technology, Virgina Beach, VA, May, pp. 623– 634, 2000.Google Scholar
  6. 6.
    J. Keller and P. Grabill, Vibration monitoring of a UH-60A main transmission planetary carrier faul, in Proceedings of the American Helicopter Society 59th Annual Forum, Phoenix, AZ, May, pp. 1–11, 2003.Google Scholar
  7. 7.
    P. McFadden and J. Smith, An explanation for the asymmetry of the modulation sidebands about the tooth meshing frequency in epicyclic gear vibration, Proceedings Institution of Mechanical Engineers, Part C: Mechanical Engineering Science 199(1), 65–70, 1985.CrossRefGoogle Scholar
  8. 8.
    A. Szczepanik, Time synchronous averaging of ball mill vibrations, Mechanical Systems and Signal Processing 3, 99–107, January 1989.CrossRefGoogle Scholar
  9. 9.
    R. Patrick, A model-based framework for fault diagnosis and prognosis of dynamical system with an application to helicopter transmissions, PhD Proposal, School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, September 2006.Google Scholar
  10. 10.
    M. Orchard, A particle filtering-based framework for on-line fault diagnosis and failure prognosis, PhD Proposal, School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 2006.Google Scholar
  11. 11.
    J. Antoni, Blind separation of vibration components: Principles and demonstrations, Mechanical Systems and Signal Processing 19, 1166–1180, 2005.CrossRefGoogle Scholar
  12. 12.
    G.R. Ayers and J.C. Dainty, Iterative blind deconvolution method and its applications, Optics Letters 13, 547–549, July 1988.CrossRefGoogle Scholar
  13. 13.
    G. Gelle, M. Colas and G. Delaunay, Blind sources separation applied to rotating machines monitoring by acoustical and vibrations analysis, Mechanical Systems and Signal Processing 14, 427–442, July 2000.CrossRefGoogle Scholar
  14. 14.
    B. Klamecki, Use of stochastic resonance for enhancement of low-level vibration signal components, Mechanical Systems and Signal Processing 19, 223–237, 2005.CrossRefGoogle Scholar
  15. 15.
    G. Hillerstrom, Adaptive suppression of vibrations — A repetitive control approach, IEEE Trans. Control Systems Technology 4(1), 72–78, 1996.CrossRefGoogle Scholar
  16. 16.
    J. Antoni and R. Randall, Unsupervised noise cancellation for vibration signals: Part I — Evaluation of adaptive algorithms, Mechanical Systems and Signal Processing 18, 89–101, 2004.CrossRefGoogle Scholar
  17. 17.
    D. Kundur and D. Hatzinakos, A novel blind deconvolution scheme for image restoration using recursive filtering, IEEE Trans. Signal Processing 26, 375–390, February 1998.CrossRefMathSciNetGoogle Scholar
  18. 18.
    M.Z.R. Peled and S. Braun, A blind deconvolution separation of multiple sources, with application to bearing diagnostics, Mechanical Systems and Signal Processing 19, 1181–1195, 2005.CrossRefGoogle Scholar
  19. 19.
    A.K. Nandi, D. Mampel and B. Roscher, Blind deconvolution of ultrasonic signals in nondestructive testing applications, IEEE. Trans. Signal Processing 45, 1382–1390, May 1997.CrossRefGoogle Scholar
  20. 20.
    R. Prost and R. Goutte, Discrete constrained iterative deconvolution with optimized rate of convergence, Signal Processing 7, 209–230, December 1984.CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer Science + Business Media B.V. 2009

Authors and Affiliations

  • Bing Zhang
    • 1
  • Taimoor Khawaja
    • 1
  • Romano Patrick
    • 1
  • George Vachtsevanos
    • 1
  • Marcos Orchard
    • 1
    • 2
  • Abhinav Saxena
    • 1
  1. 1.School of Electrical & Computer EngineeringGeorgia Institute of TechnologyAtlantaUSA
  2. 2.Department of Electrical EngineeringUniversity of ChileSantiagoChile

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