WAVELET-BASED ALGORITHM FOR DETECTION OF BEARING FAULTS IN A GAS TURBINE ENGINE

Authors

  • Sergiy Enchev National Aviation University, Kyiv, Ukraine
  • Sergiy Tovkach National Aviation University, Kyiv, Ukraine

DOI:

https://doi.org/10.18372/2306-1472.59.6798

Keywords:

bearing faults, complex Morlet wavelet, gas turbine engine, wavelet scalogram and ridges

Abstract

Presented is a gas turbine engine bearing diagnostic system that integrates information from various advanced vibration analysis techniques to achieve robust bearing health state awareness. This paper presents a computational algorithm for identifying power frequency variations and integer harmonics by using wavelet-based transform. The continuous wavelet transform with  the complex Morlet wavelet is adopted to detect the harmonics presented in a power signal. The algorithm based on the discrete stationary wavelet transform is adopted to denoise the wavelet ridges.

Author Biographies

Sergiy Enchev, National Aviation University, Kyiv, Ukraine

Enchev Sergiy (1979). Candidate of Engineering. Associate Professor.
Automation and Energy Management Department, National Aviation University, Kyiv, Ukraine.
Education: National Aviation University, Kyiv, Ukraine (2002).
Research area: methods of synthesis and diagnostics of power systems control aircraft

Sergiy Tovkach, National Aviation University, Kyiv, Ukraine

Tovkach Sergiy (1989). Postgraduate student.
Automation and Energy Management Department, National Aviation University, Kyiv, Ukraine.
Education: Cherkasy National University named after Bohdan Khmelnytsky, Cherkasy, Ukraine (2011).
Research area: the automated processing of measurement data in electronic control systems of aircraft gas turbine engine.

References

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Carmona, R. A.; Hwang, W. L.; Torresan, B. Characterization of signals by the ridges of their wavelet transforms. IEEE Transactions on Signal Processing. 2005. Vol. 45, N 10. P. 2586–2590.

Defect detection of rolling bearings (translated materials of company IRD). Available from Internet: <http://www.vibration.ru/obnar_defekt.shtml> (in Russian).

Huang, S.-J.; Hsieh, C.-T.; Huang, C.-L. Application of Morlet wavelets to supervise power system disturbances. IEEE Transactions on Power Delivery. 2009. Vol. 14, N. 1. P. 235–241.

Pham, V. L.;Wong, K. P. Wavelet-transform-based algorithm for harmonic analysis of power system waveforms. IEE Proceedings: Generation, Transmission and Distribution. 2007. Vol. 146, N 3. P. 249–254.

Sinyakov, A. N.; Shamayrdanov, F. A. Automatic control system of aircraft and propulsion systems. Mechanical engineering. 1991. P. 320 (in Russian).

Tereshchenko, J. M.; Kulik, M. S.; Panin, V. V. Theory of aviation gas turbine engine. 2005. P. 500 (in Russian).

Zhernakov S. V. Algorithms for control and diagnostics of aircraft gas turbine engine conditions onboard implementation based on neural network technology. 2010. N 3 (38). P. 42-56 (in Russian).

Published

04-07-2014

How to Cite

Enchev, S., & Tovkach, S. (2014). WAVELET-BASED ALGORITHM FOR DETECTION OF BEARING FAULTS IN A GAS TURBINE ENGINE. Advances in Aerospace Technology, 59(2), 88–96. https://doi.org/10.18372/2306-1472.59.6798

Issue

Section

MODERN AVIATION AND SPACE TEHNOLOGY