Diagnostics of Local Tooth Damage in Gears by the Wavelet Technology
The chipped gear tooth appearance is normally a result of the initial fatigue damage in a tooth. It is a special gear failure mode and differs from local fatigue damage of gear teeth. Therefore, diagnosis of chipped gear tooth requires a special investigation. Recently, the novel gear damage diagnosis technology, based on the wavelet transform was proposed and successfully applied for diagnosis of the early stage fatigue damage. The proposed technology is applied in this study for diagnostics of a partly-missing (chipped) tooth in a gear of the Machine Fault Simulator (MFS). The advanced automatic technology for the time synchronous averaging of the raw gear vibrations has been employed in this study; this technology does not require speed data. An advanced decision making technique based on use of the likelihood ratio allowed for the continuous correct diagnosis of chipped teeth throughout the recorded data without false alarms and missed detections. The likelihood ratio was obtained using the Gaussian models of the data for classes “undamaged” and “damaged”.
chipped tooth, likelihood ratio, gear damage diagnostics, wavelet diagnosis technology
Choy, F. K., Polyshchuk, V., Zakrajsek, J. J., Handschuh, R. F., & Townsend, D. P. (1996). Analysis of the Effects of Surface Pitting and Wear on the Vibration of a Gear Transmission System. Tribology International, vol. 29(1), pp. 77–83.
Combet, F., & Gelman, L. (2007). An Automated Methodology for Performing Time-Synchronous Averaging of a Gearbox Signal Without Speed Sensor. Mechanical Systems and Signal Processing, vol. 21(6), pp. 2590-2606.
Combet, F., & Gelman, L. (2009). Optimal Filtering of Gear Signals for Early Damage Detection Based on the Spectral Kurtosis. Mechanical Systems and Signal Processing, vol. 23(3), pp. 652–668.
Combet, F., Gelman, L., Anuzis, P., & Slater, R. (2009). Vibration detection of local gear damage by advanced demodulation and residual techniques. Proc. IMechE, vol. 223 Part G: J. Aerospace Engineering, pp.507-514.
Combet, F., Gelman, L., & LaPayne, G. (2012). Novel Detection of Local Tooth Damage in Gears by the Wavelet Bicoherence. Mechanical Systems and Signal Processing, vol. 26, pp. 218-228.
Dalpiaz, G., Rivola, A., & Rubini, R. (2000). Effectiveness and Sensitivity of Vibration Processing Techniques for Local Fault Detection in Gears. Mechanical Systems and Signal Processing, vol. 14(3), pp. 387–412.
Endo, H., & Randall, R. B. (2007). Enhancement of Autoregressive Model Based Gear Tooth Fault Detection Technique by the Use of Minimum Entropy Deconvolution Filter. Mechanical Systems and Signal Processing, vol. 21(2), pp. 906–919.
Feng, Z., Liang, M., Zhang, Y. & Hou, S. (2012). Fault Diagnosis for Wind Turbine Planetary Gearboxes via Demodulation Analysis Based on Ensemble Empirical Mode Decomposition and Energy Separation. Renewable Energy, vol. 47, pp. 112-126.
Forrester, B. D. (1996). Advanced Vibration Analysis Techniques for Fault Detection and Diagnosis in Geared Transmission Systems. Ph. D. dissertation. Swinburne University of Technology, Melbourne, Australia.
Gelman, L., Zimroz, R., Birkel, J., Leigh-Firbank, H., Simms, D., Waterland, B., & Whitehurst, G. (2005). Adaptive Vibration Condition Monitoring Technology for Local Tooth Damage in Gearboxes. Insight Int. J. Non-Destructive Testing and Condition Monitoring, vol. 47(8), pp. 461–464.
Gryllias, K. C., Gelman, L., Shaw, B. & Vaidhianathasamy, M. (2010). Local Damage Diagnosis in Gearboxes Using Novel Wavelet Technology. Insight, vol. 52(8), pp. 437-441.
Halima, E. B., Shoukat Choudhuryb, M. A. A., Shaha, S. L., & Zuoc, M. J. (2008). Time Domain Averaging Across all Scales: a Novel Method for Detection of Gearbox Faults. Mechanical Systems and Signal Processing, vol.22(2), pp.261–278.
Jiang, L., Liu, Y., Li, X. & Tang, S. (2011). Using Bispectral Distribution as a Feature for Rotating Machinery Fault Diagnosis. Measurement, vol. 44, pp. 1284-1292.
Lebold, M., McClintic, K., Campbell, R., Byington, C., & Maynard, K. (2000). Review of Vibration Analysis Methods for Gearbox Diagnostics and Prognostics. Proc. of the 54th Meeting of the Society for Machinery Failure Prevention Technology, Virginia Beach, VA, May 1-4, 2000, p. 623-634.
Lee, J. Y., & Nandi, A. K. (2000). Extraction of Impacting Signals Using Blind Deconvolution. Journal of Sound and Vibration, vol. 232(5), pp. 945–962.
Lee, S. K., & White, P. R. (1998). The Enhancement of Impulsive Noise and Vibration Signals for Fault Detection in Rotating and Reciprocating Machinery. Journal of Sound and Vibration, vol. 217(3), pp. 485–505.
Lei, Y., Zuo, M. J., He, Z., & Zi, Y. (2010). A multidimensional hybrid intelligent method for gear fault diagnosis. Expert Systems with Applications, vol. 37 (2), pp. 1419–1430.
Lin, J., & Zuo, M. J. (2003). Gearbox Fault Diagnosis Using Adaptive Wavelet Filter. Mechanical Systems and Signal Processing, vol. 17(6), pp. 1259–1269.
Loutridis, S. J. (2006). Instantaneous Energy Density as a Feature for Gear Fault Detection. Mechanical Systems and Signal Processing, vol. 20(5), pp. 1239–1253.
Maynard, K. P. (1999). Interstitial Processing: The Application of Noise Processing to Gear Fault Detection. International Conference on Condition Monitoring, University of Wales Swansea, April 12-15, pp. 77-86.
McFadden, P. D. (1986). Detecting Fatigue Cracks in Gears by Amplitude and Phase Demodulation of the Meshing Vibration. Journal of Vibration, Acoustics, Stress, and Reliability in Design, vol. 108, pp. 165-170.
McFadden, P. D. (1987). Examination of a Technique for the Early Detection of Failure in Gears by Signal Processing of the Time Domain Average of the Meshing Vibration. Mechanical Systems and Signal Processing, vol. 1(2), pp. 173–183.
Stewart, R. M. (1977). Some Useful Data Analysis Techniques for Gearbox Diagnostics. Institute of Sound and Vibration Research, Paper MHM/R/10/77.
Wang, W. Q., Ismail, F., & Golnaraghi, M. F. (2001). Assessment of Gear Damage Monitoring Techniques Using Vibration Measurements. Mechanical Systems and Signal Processing, vol. 15(5), pp. 905–922.
Wang, W. J., & McFadden, P. D. (1993). Early Detection of Gear Failure by Vibration Analysis—I. Calculation of the Time-Frequency Distribution. Mechanical Systems and Signal Processing, vol. 7(3), pp. 193–203.
Wang, W. J. & McFadden, P. D. (1996). Application of Wavelets to Gearbox Vibration Signals for Fault Detection. Journal of Sound and Vibration, vol. 192 (5), pp. 927-939.
Wang, W., & Wong, A. K. (2002). Autoregressive Model- Based Gear Fault Diagnosis. ASME Journal of Vibration and Acoustics, vol. 124, pp. 172–179.
Webb, A. (1999). Statistical pattern recognition. London: Arnold.
Yan, B. F., Miyamoto, A. & Brühwiler, E. (2006). Wavelet Transform-Based Modal Parameter Identification Considering Uncertainty, Journal of Sound and Vibration, vol. 291, pp. 285–301.
Zamanian, A. H. & Ohadi, A. (2011). Gear Fault Diagnosis Based on Gaussian Correlation of Vibrations Signals and Wavelet Coefficients. Applied Soft Computing, vol. 11, pp. 4807-4819.