A New Acoustic Emission Sensor Based Gear Fault Detection Approach
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
In order to reduce wind energy costs, prognostics and health management (PHM) of wind turbine is needed to reduce operations and maintenance cost of wind turbines. The major cost on wind turbine repairs is due to gearbox failure. Therefore, developing effective gearbox fault detection tools is important in the PHM of wind turbine. PHM system allows less costly maintenance because it can inform operators of needed repairs before a fault causes collateral damage happens to the gearbox. In this paper, a new acoustic emission (AE) sensor based gear fault detection approach is presented. This approach combines a heterodyne based frequency reduction technique with time synchronous average (TSA) and spectral kurtosis (SK) to
process AE sensor signals and extract features as condition indictors for gear fault detection. Heterodyne techniques commonly used in communication are used to preprocess the AE signals before sampling. By heterodyning, the AE signal frequency is down shifted from MHz to below 50 kHz. This reduced AE signal sampling rate is comparable to that of vibration signals. The presented approach is validated using seeded gear tooth crack fault tests on a notational split torque gearbox. The approach presented in this paper is physics based and the validation results have showed that it could effectively detect the gear faults.
##plugins.themes.bootstrap3.article.details##
time synchronous average, gear fault detection, acoustic emission sensors, spectrum kurtosis
Al-Ghamd, A.M. and Mba, D. (2006), “A comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect size”, Mechanical Systems and Signal Processing, Vol. 20, No. 7, pp. 1537 – 1571.
Antoni J. (2006), “The spectral kurtosis: a useful tool for characterizing non-stationary signals”, Mechanical Systems and Signal Processing, Vol. 20, No. 2, pp. 282–307.
Antoni J. and Randall R.B. (2006), “The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines”, Mechanical Systems and Signal Processing, Vol. 20, No. 2, pp. 308–331.
Bonnardot F., Badaoui M. El, Randall R.B., Daniere J., and Guillet F. (2005), “Use of the acceleration signal of a gearbox in order to perform angular resampling (with limited speed fluctuation) ”, Mechanical Systems and Signal Processing, Vol. 19, No. 4, pp. 766 - 785.
Braun S. (1975), “The exaction of periodic waveforms by time domain averaging”, Acustica, Vol. 32, No.2, pp. 69 - 77.
Capdevielle V., Servie`re C., and Lacoume J.-L. (1996), Blind separation of wide-band sources: application to rotating machine signals, Proceedings of the Eighth European Signal Processing Conference, vol. 3, pp. 2085–2088.
Dwyer R.F. (1983) “Detection of non-Gaussian signals by frequency domain kurtosis estimation”, Proceedings of the International Conference on Acoustic, Speech, and Signal Processing, Vol. 8, pp. 607–610.
Eftekharnejad B., Carrasco M.R., Charnley B., and Mba D. (2011), “The application of spectral kurtosis on Acoustic Emission and vibrations from a defective bearing”, Mechanical Systems and Signal Processing, Vol. 25, No. 1, pp. 266 - 284.
Gao L., Zai F., Su S., Wang H., Chen P., and Liu L., (2011), “Study and Application of Acoustic Emission Testing in Fault Diagnosis of Low-Speed Heavy-Duty Gears”, Sensor, Vol.11, No.1, pp. 599 – 611.
He D., Li R., Zhu J., and Zade M. (2011), “Data Mining Based Full Ceramic Bearing Fault Diagnostic System Using AE Sensors”, IEEE Transactions on Neural Network, Vol. 22, No. 12, pp. 2022 – 2031.
Kaiser J. F. (1990), “On Teager’s Energy Algorithm and Its Generalization to Continuous Signals”, Proc. 4th IEEE Digital Signal Processing Workshop, Mohonk (New Palts), NY.
Kilundu B., Chiementin X., Duez J. and Mba D., (2011), “Cyclostationarity of Acoustic Emissions (AE) for monitoring bearing defects”, Mechanical Systems and Signal Processing, Vol. 25, No. 6, pp.2061 - 2072.
Lebold, M.; McClintic, K.; Campbell, R.; Byington, C.; Maynard, K. (2000), Review of Vibration Analysis Methods for Gearbox Diagnostics and Prognostics, Proceedings of the 54th Meeting of the Society for Machinery Failure Prevention Technology, Virginia Beach, VA, May 1-4, pp. 623-634.
Li R. and He D. (2012), “Rotational Machine Health Monitoring and Fault Detection Using EMD-Based Acoustic Emission Feature Quantification”, IEEE Transactions on Instrumentation and Measurement , Vol. 61, No.4 , pp. 990 – 1001.
Mba D. (2003), “Acoustic Emissions and monitoring bearing health”, Tribology Transactions, Vol.46, No.3, pp. 447 – 451.
McFadden P.D. (1987), “A revised model for the extraction of periodic waveforms by time domain averaging”, Mechanical Systems and Signal Processing, Vol.1, No.1, pp. 83 – 95.
McFadden P.D. , Toozhy M.M. (2000), “Application of synchronous averaging to vibration monitoring of rolling element bearings”, Mechanical Systems and Signal Processing, Vol. 14, No. 6, pp.891–906.
Pandya D. H., , Upadhyay S.H., and Harsha S.P., (2013), “Fault diagnosis of rolling element bearing with intrinsic mode function of acoustic emission data using APF-KNN”, Expert Systems with Applications, http://dx.doi.org/10.1016/j.eswa.2013.01.033
Teager H. M. and Teager S. M. (1992), “Evidence for Nonlinear Sound Production Mechanisms in the Vocal Tract”, in Speech Production and Speech Symp. Time- Frequency and Time-Scale Analysis, Victoria, British Columbia, Canada, pp. 345-348.
Večeř P., Kreidl M. and Šmíd R. (2005), “Condition Indicators for Gearbox Condition Monitoring Systems”, Acta Polytechnica, Vol. 45, No. 6, pp. 35 - 43.
Zakrajsek J.J., Townsend D.P., Decker H.J. (1993), “An analysis of gear fault detection methods as applied to pitting fatigue failure data”, Technical Report NASA TM-105950, AVSCOM TR-92-C-035, NASA and the US Army Aviation Systems Command.