Acoustic Emissions (AE) are stress waves produced by the sudden internal stress redistribution of material caused by changes in the internal structure of the material. Possible causes of these changes are crack initiation and growth, crack opening/closure, or pitting in monolithic materials (gear/ bearing material). Where as vibration can measure the effect of damage, AE is a direct measure of damage. Unfortunately, AE methodologies suffer from the need of high sample rates (4 to 10 Msps) and relatively immature algorithms for condition indictors (CI). This paper hypothesizes that the AE signature is the result of some forcing function (e.g. periodic motion in the case of rotating machinery). By using analog signal processing to demodulating the AE signature, one can reconstruct the information carried (e.g. modulation) by the AE signature and provide improved diagnostics. As most on-line condition monitoring systems are embedded system, analog signal processing techniques where used which reduce the effective sample rate needed to operate on the AE signal to those similarly found in traditional vibration processing systems. Further, by implementing another signal processing technique, time synchronous averaging, the AE signal is further enhanced. This allowed, for the first time, an AE signal to be used to identify a specific component within gearbox. This processing is tested on a split torque gearbox and results are presented.
How to Cite
Time Synchronous Averaging (TSA), gear fault detection, Acoustic Emissions, Envelope Analysis
Liptai, R., Harris, D., Engle, R., and Tatro, C., (1970). Acoustic Emissions Techniques in Materials Research, Proceedings of the Symposium on Advanced Experimental Techniques in Mechanics of Materials.
Miller, R.K., Hill, E.v.K., and Moore, P.O., (2005).Nondestructive Testing Handbook, 3rd Ed., Vol. 6. Acoustic Emission Testing. Columbus, OH: American Society for Nondestructive Testing, p. 32
Barsoum, F. F., Suleman, J., Korcak, A., and Hill, E. V., (2009). Acoustic Emission Monitoring and Fatigue Life Prediction in Axially Loaded Notched Steel Specimens, J. Acoustic Emission, 27.
Abouel-seoud S. A., Lemosry, M., (2012). Enhancement of Signal Denoising and Fault Detection in Wind Turbine Planetary Gearbox Using Wavelet Transform, International Journal of Science and Advanced Technology, Volume 2 No 5 May.
Gu, D. S., and Choi, B. K., (2011). “Machinery Faults Detection Using Acoustic Emission Signal, Acoustic Waves - From Microdevices to Helioseismology, ISBN 978-953-307-572-3.
Horowitz, P., Winfield, H., (1998). The Art of Electronics, Cambridge University Press.
AD532 data sheet, “Internally Trimmed Integrated Circuit Multiplier”, www.analog.com
McFadden, P. (1987). A revised model for the extraction of periodic waveforms by time domain averaging. Mechanical Systems and Signal Processing 1 (1), 83-95
Bechhoefer, E., Kingsley, M. (2009). A Review of Time Synchronous Average Algorithms. Annual Conference of the Prognostics and Health Management Society
McFadden, P., Smith, J., (1985), A Signal Processing Technique for detecting local defects in a gear from a signal average of the vibration. Proc Instn Mech Engrs.
ISO 10825. (2007) Gears -- Wear and damage to gear teeth - - Terminology
Zakrajsek, J. Townsend, D., Decker, H. (1993). An Analysis of Gear Fault Detection Method as Applied to Pitting Fatigue Failure Damage. NASA Technical Memorandum 105950.
Li, R., Seckiner, S. U., He, D., Bechhoefer, E., Menon, P., (2012) Gear Fault Location Detection for Split Torque Gearbox Using AE Sensor, IEEE Transactions on Systems, Man, and
Cybernetics – Part C:, Applications and Reviews” IEEE 1094-6977.
Bechhoefer, E., Wadham-Gagnon, M., Boucher, B., (2012). Initial Condition Monitoring Experience on a Wind Turbine, PHM Society Annual Forum, Minneapolis, MN.
Wackerly, D., Mendenhall, W., Scheaffer, R.,(1996), Mathematical Statistics with Applications, Buxbury Press, Belmont.
Bechhoefer, E., Li, R., He, D., (2009). Quantification of Condition Indicator Performance on a Split Torque Gearbox, American Helicopter Society 65th Annual Forum, Grapevine, Texas, May 27-29
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