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Excursions in Harmonic Analysis, Volume 1 pp 433–460Cite as

Birkhäuser

Eddy Current Sensor Signal Processing for Stall Detection

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Part of the book series: Applied and Numerical Harmonic Analysis ((ANHA))

Abstract

This chapter presents algorithms that use data from eddy-current sensors mounted in the engine casing for the purpose of gas turbine engine stability monitoring. To date, most signal-processing techniques using blade tip sensors have been limited to simple parametric measurements associated with the sensor waveform, for example measurement of zero-crossing locations for time of arrival information or maxima for tip clearance information. Using this type of parametric information, many computations require more than one sensor per stage. The use of a minimal number of sensors is an extremely important practical consideration since each pound that is added to an aircraft engine adds considerable costs over the life cycle of the engine. Because of this we have focused on developing algorithms that allow the reduction in the number of sensors needed for fault prognosis. Using our algorithms we have been able to demonstrate the detection of stall cell precursors using a single ECS. These algorithms have been demonstrated in real time in tests at the NASA Glenn W8 single-stage axial-flow compressor facility. The rotor tested, designated NASA Rotor 67, is a fan with 22 blades.

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Notes

  1. 1.

    A first-order mode would have a wavelength equal to the circumference of the compressor; a second-order mode would have a wavelength equal to half of the circumference of the compressor, and so on.

  2. 2.

    Stage mismatching can limit the disturbance to a particular axial region.

  3. 3.

    Moore and Greitzer developed a model for low-speed compressors which was later extended to for analysis of high-speed machines by Feullner et al. [30] and others.

  4. 4.

    In contrast, the pressure perturbation at a given circumferential position is correlated to that at any other position.

  5. 5.

    Because the Fourier coefficients are complex functions of time, the PSD’s are not symmetric with respect to zero frequency.

  6. 6.

    Since noise is generally a broadband, low-power disturbance, the wavelet transform of the noise would have a low-amplitude contribution for each frequency and time. Thus setting to zero any value below a low-amplitude threshold is usually an effective means of reducing noise.

  7. 7.

    The wavelet transform can be implemented as a bank of filters, where the frequency bands are the outputs associated with a particular filter.

  8. 8.

    For more details on wavelet analysis refer to [41].

  9. 9.

    The blade numbering begins with 1 after the rising edge of the synchronization pulse. This numbering may be inconsistent with the physical blade numbers from the experiment, which are unavailable to us at this time.

  10. 10.

    The recirculation tests were performed as part of another testing effort and the results of the stability performance are reported elsewhere.

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Acknowledgments

There are very few engine test facilities and fewer opportunities for a small company to participate in testing. Making this testing opportunity possible involved the help of many people. The authors would like to thank Carole Ginty, Vithal Dalsania, Michael Hathaway, Joe Veres, Sanjay Garg, and Osvlado Rivera for allowing us to piggyback on their tests under the UEET Program and also Tony Strazisar for coordination of the testing and testing advice and Dennis Culley, Jose Gonzales, Helmi Abulaban, Scott Thorp, Sue Prahst, Rick Brokopp, and Bruce Wright for their essential roles in preparing for and conducting the test. We would also like to thank Erdal Unver at GDAIS and Ravi Ravindranath at NAVAIR for lending sensors and electronics for the testing. Also Mike Dowell at GDAIS who provided data from previous engine tests to facilitate algorithm development. This work has been funded by NASA and NSF SBIR programs, contract NAS#-99002, and NSF-0110316, NSF-0216021, respectively.

Author information

Authors and Affiliations

  1. Organization TRX Systems, Inc, Greenbelt, MD, USA

    Carole Teolis

  2. Organization Techno-Sciences, Inc, Beltsville, MD, USA

    David Gent & Christine Kim

  3. Organization Teolis Consulting, Glenn Dale, MD, USA

    Anthony Teolis

  4. Organization Aurora Flight Sciences, Cambridge, MA, USA

    James Paduano

  5. Organization NASA Glenn Research Center, Cleveland, OH, USA

    Michelle Bright

Authors
  1. Carole Teolis
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  2. David Gent
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  3. Christine Kim
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  4. Anthony Teolis
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  6. Michelle Bright
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Corresponding author

Correspondence to Carole Teolis .

Editor information

Editors and Affiliations

  1. , Department of Mathematics, University of Maryland, Norbert Wiener Center, College Park, 20742-0001, Maryland, USA

    Travis D. Andrews

  2. Norbert Wiener Center, Department of Mathematics, University of Maryland, College Park, 20742, Maryland, USA

    Radu Balan

  3. Department of Mathematics, University of Maryland, College Park, 20742-4015, Maryland, USA

    John J. Benedetto

  4. Department of Mathematics, University of Maryland, College Park, 20742-4015, Maryland, USA

    Wojciech Czaja

  5. , Department of Mathematics, University of Maryland, College Park, 20742, Maryland, USA

    Kasso A. Okoudjou

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Teolis, C., Gent, D., Kim, C., Teolis, A., Paduano, J., Bright, M. (2013). Eddy Current Sensor Signal Processing for Stall Detection. In: Andrews, T., Balan, R., Benedetto, J., Czaja, W., Okoudjou, K. (eds) Excursions in Harmonic Analysis, Volume 1. Applied and Numerical Harmonic Analysis. Birkhäuser, Boston. https://doi.org/10.1007/978-0-8176-8376-4_21

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