Detection/Diagnosis of Chipped Tooth in Gears by the Novel Residual Technology
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Published
Mar 23, 2021
L. Gehnan
I. Jennions
I. Petrunin
Abstract
The no,·el residual technology is applied for the detection/diagnosis of pa11ly-missing ( chipped) tooth in a gear of the machine fault simulator (MFS) produced by SpectraQuest (USA). The automated sensor-less technique is implemented for the speed esti1nation. This technique estimates the speed data from raw vibration data using the nan-ow-band demodulation of the mesh component. providing: that an approxiniate nmni112 speed and munber of teeth are known. An adYanced technique based on the likelihood ratio is used for decision making. The noYel technology is compared with the conwntional technique. the classical residual technology. For both technologies, the gear fault has been continuously diagnosed thromtl1out the whole test duration without fats; alanns and ... missed detections. The use of the no,·el residual technology in comparison to the classical residual technology proYides higher probability of the coll'ect damage detection and f;ster damage diagnosis.
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Keywords
damage detection, gearbox, chipped tooth, the residual technology, likelihood ratio
References
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.
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.
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.
Randall, R. B. (1982). A new method of modeling gear faults. ASME Journal of Mechanical Design , vol. 104, pp. 259-267.
Wang, W. Q., Ismail, F., & Golnaragh i, M. F. (2001). Assessment of gear damage monitoring techniques using vibration measurements. Mechanical Systems and Signal Processing , vol. 15(5), pp. 905-922.
Brie , D., Tomczak, M., Oehlmann, H., & Richard, A. (1997). Gear crack detection by adaptive amplitude and phase modulation. Mechanical Systems and Signal Processing , vol. 11(1), pp. 149-167.
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.
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.
Wang, W., & Wong, A. K. (2002). Autoregressive model-based gear fault diagnosis. ASME Journal of Vibration and Acoustics, vol. 124, pp. 172-179.
Martin, N., Jaussaud, P., & Combet, F. (2004). Close shock detection using time-frequency Prony modeling. Mechanical Systems and Signal Processing, vol. 18(2), pp. 235-261.
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.
Endo, H., & Randall, R. B. (2007). Enhancement of autoregressive model based gear tooth fault detection techn ique by the use of minimum entro py deconvolution filter. Mechanical Systems and Signal Processing, vol. 21(2), pp. 906-919.
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.
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.
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.
Wang, W. J. & McFadden, P. D. (1996). Applicati on of wavelets to gearbox vibration signals for fault detection . Journal of Sound and Vibration, vol. 192 (5), pp. 927-939.
Loutridis, S. J. (2006). Instantaneous ene rgy densi ty as a feature for gear fault detection. Mechanical Systems and Signal Processing, vol. 20(5), pp. 1239-1253.
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.
Dalpi az, G., Rivola, A., & Rubini, R. (2000). Effectiveness and sens1t1v1ty of vibration processing techniques for local fault detection in gears. Mechanical Systems and Signal Processing, vol. 14(3), pp. 387-412.
Lin, J., & Zuo, M. J. (2003). Gearbox fault diagnosis using adaptive wavelet filter. Mechanical Systems and Signal Processing, vol. 17(6), pp. 1259-1269.
Stewart, R. M. (1977). Some useful data analysis techniques for gearbox diagnostics. Institute of Sound and Vibration Research, Paper MHM/R/10/77.
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.
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.
Webb, A. (1999). Statistical pattern recognition. London: Arnold.
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.
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.
Randall, R. B. (1982). A new method of modeling gear faults. ASME Journal of Mechanical Design , vol. 104, pp. 259-267.
Wang, W. Q., Ismail, F., & Golnaragh i, M. F. (2001). Assessment of gear damage monitoring techniques using vibration measurements. Mechanical Systems and Signal Processing , vol. 15(5), pp. 905-922.
Brie , D., Tomczak, M., Oehlmann, H., & Richard, A. (1997). Gear crack detection by adaptive amplitude and phase modulation. Mechanical Systems and Signal Processing , vol. 11(1), pp. 149-167.
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.
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.
Wang, W., & Wong, A. K. (2002). Autoregressive model-based gear fault diagnosis. ASME Journal of Vibration and Acoustics, vol. 124, pp. 172-179.
Martin, N., Jaussaud, P., & Combet, F. (2004). Close shock detection using time-frequency Prony modeling. Mechanical Systems and Signal Processing, vol. 18(2), pp. 235-261.
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.
Endo, H., & Randall, R. B. (2007). Enhancement of autoregressive model based gear tooth fault detection techn ique by the use of minimum entro py deconvolution filter. Mechanical Systems and Signal Processing, vol. 21(2), pp. 906-919.
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.
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.
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.
Wang, W. J. & McFadden, P. D. (1996). Applicati on of wavelets to gearbox vibration signals for fault detection . Journal of Sound and Vibration, vol. 192 (5), pp. 927-939.
Loutridis, S. J. (2006). Instantaneous ene rgy densi ty as a feature for gear fault detection. Mechanical Systems and Signal Processing, vol. 20(5), pp. 1239-1253.
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.
Dalpi az, G., Rivola, A., & Rubini, R. (2000). Effectiveness and sens1t1v1ty of vibration processing techniques for local fault detection in gears. Mechanical Systems and Signal Processing, vol. 14(3), pp. 387-412.
Lin, J., & Zuo, M. J. (2003). Gearbox fault diagnosis using adaptive wavelet filter. Mechanical Systems and Signal Processing, vol. 17(6), pp. 1259-1269.
Stewart, R. M. (1977). Some useful data analysis techniques for gearbox diagnostics. Institute of Sound and Vibration Research, Paper MHM/R/10/77.
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.
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.
Webb, A. (1999). Statistical pattern recognition. London: Arnold.
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Technical Briefs