Application of wavelet and Wigner analysis to gas turbine vibration signal processing

Gregory A. Harrison; Iztok Koren; Michael P. Lewis; Fred J. Taylor

doi:10.1117/12.304898

26 March 1998 Application of wavelet and Wigner analysis to gas turbine vibration signal processing

Gregory A. Harrison, Iztok Koren, Michael P. Lewis, Fred J. Taylor

Proceedings Volume 3391, Wavelet Applications V; (1998) https://doi.org/10.1117/12.304898
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

Abstract

The analysis of gas turbine vibration is enhanced by the use of wavelet characterization and Wigner-Ville distribution processing to represent vibration features. The output of vibration sensors is digitized and the signal is processed by these means to identify signals associated with damage and progressive turbine wear. Wavelet processing provides fast transient detection useful in minimizing subsequent damage to turbine components through quick reaction. During turbine operation, short duration features appear, such as rotating stall conditions, that are well suited for detection with wavelet techniques. The Wigner-Ville distribution provides very accurate determination of vibration amplitudes in the nonstationary environment encountered in the use of gas turbines for vehicular propulsion. The Wigner-Ville distribution is described, and techniques for obtaining highly accurate amplitude information in the presence of noise and nonstationarity are presented. The wavelet transform is capable of making trade- offs between time and frequency resolutions, a property that makes it appropriate for the analysis for the analysis of nonstationary signals. Its ability to 'zoom in' on short lived high frequency phenomena is particularly attractive for the analysis of transients. Features of interest can be characterized form the evolution of the transform coefficients across distinct scales. Different types of wavelet transforms for an efficient time-frequency processing of the vibration signals are investigated. The resulting wavelet and Wigner features are used as inputs to a neural net which combine them with system health parameters. The result is a viable turbine monitor system, which can respond to long and short term events in a reliable and responsive manner.

Citation Download Citation

Gregory A. Harrison, Iztok Koren, Michael P. Lewis, and Fred J. Taylor "Application of wavelet and Wigner analysis to gas turbine vibration signal processing", Proc. SPIE 3391, Wavelet Applications V, (26 March 1998); https://doi.org/10.1117/12.304898

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