Gear Fault Diagnostics Using Extended Phase Space Topology
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Abstract
This paper applies a novel feature extraction method called Extended Phase Space Topology (EPST) in order to diagnose various faults in a gear-train system. The EPST method, that our research team has been developing, is based on characterizing the vibration data using the topology of phase space, computing its density distribution and then expanded in a series of orthogonal functions. The resulting coefficients are subsequently used in a machine learning algorithm. For this study, multiple test gears with different health conditions (such as a healthy gear and defective gears with root crack on one tooth, multiple cracks on five teeth and missing tooth) are studied. The vibration data of a gear-train is measured by a triaxial accelerometer installed on the test. Results indicate that EPST is efficient in diagnosing the status of the health of the gear system and characterizing the dynamic behavior. Moreover, the EPST procedure does not require a priori knowledge about the dynamics of the system. EPST needs no noise reduction, signal prepossessing, feature ranking or selection, and therefore can easily be applied in a relatively automated process.
How to Cite
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feature extraction, gear fault detection, Machinery diagnostics
Bishop, C. (2006). Pattern recognition and machine learning. Springer-Verlag, New York.
Chad, E. F. (1998). Synchronous sampling sideband orders from helical planetary gear sets (Unpublished master’s thesis). Blacksburg, Virginia,.
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, 21(6), 2590 - 2606. doi: fhttps://doi.org/10.1016/j.ymssp.2006.12.006g
Dalpiaz, G., Rivola, A., & Rubini, R. (2000, May). Effectiveness and Sensitivity of Vibration Processing Techniques for Local Fault Detection in Gears. Mechanical Systems and Signal Processing, 14, 387-412. doi: 10.1006/mssp.1999.1294
Haj Mohamad, T., Kwuimy, C. K., & Nataraj, C. (2017). Discrimination of Multiple Faults in Bearings Using Density-Based Orthogonal Functions of the Time Response. International Design Engineering Technical Conferences and Computers and Information in Engineering Conference.
Hong, L., Qu, Y., Dhupia, J. S., Sheng, S., Tan, Y., & Zhou, Z. (2017). A novel vibration-based fault diagnostic algorithm for gearboxes under speed fluctuations without rotational speed measurement. Mechanical Systems and Signal Processing, 94, 14 - 32. doi: https://doi.org10.1016j.ymssp.2017.02.024
Hussain, S., & A.Gabbar, H. (2011, 05). A novel method for real time gear fault detection based on pulse shape analysis. MechanicalSystemsandSignalProcessing, 25, 12871298.
Jardine, A. K. S., Lin, D., & Banjevic, D. (2006, October). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20, 1483-1510. doi: 10.1016/j.ymssp.2005.09.012
Kwuimy, C. A. K., Kankar, P. K., Chen, Y., Chaudhry, Z., & Nataraj, C. (2015). Development of Recurrence Analysis for Fault Discrimination in Gears. International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Volume 8: 27th Conference on Mechanical Vibration and Noise.
Li, B., Zhang, X., & Wu, J. (2017). New procedure for gear fault detection and diagnosis using instantaneous angular speed. Mechanical Systems and Signal Processing, 85, 415 - 428. doi: https://doi.org/10.1016/j.ymssp.2016.08.036
Mohammed, O. D., & Rantatalo, M. (2016). Dynamic response and time-frequency analysis for gear tooth crack detection. Mechanical Systems and Signal Processing, 6667, 612 - 624. doi: https://doi.org/10.1016/j.ymssp.2015.05.015
Mohammed, O. D., Rantatalo, M., Aidanp, J.-O., & Kumar, U. (2013). Vibration signal analysis for gear fault diagnosis with various crack progression scenarios. Mechanical Systems and Signal Processing, 41(12), 176 - 195. doi: https://doi.org/10.1016/j.ymssp.2013.06.040
Peng, F., Yu, D., & Luo, J. (2011). Sparse signal decomposition method based on multi-scale chirplet and its application to the fault diagnosis of gearboxes. Mechanical Systems and Signal Processing, 25(2), 549 - 557.
Peng, Z., & Chu, F. (2004, February). Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mech. Syst. Signal Process., 18, 199221. doi: 10.1016/S0888-3270(03)00075-X
Randall, R. B. (1982). A new method of modelling gear faults. ASME Journal of Mechanical Design, 104(2), 259-267.
Randall, R. B. (2011). Vibration-based condition monitoring. John Wiley & Sons, Ltd.
Salem, A.-A. (2012). Condition monitoring of gear systems using vibration analysis (Unpublished doctoral dissertation). University of Huddersfield.
Samadani, M., Haj Mohamad, T., & Nataraj, C. (2016). Feature Extraction for Bearing Diagnostics Based on the Characterization of Orbit Plots With Orthogonal Functions. International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Volume 8: 28th Conference on Mechanical Vibration and Noise.
Samadani, M., Kwuimy, C. K., & Nataraj, C. (2013). Diagnostics of a nonlinear pendulum using computational intelligence. In Asme 2013 dynamic systems and control conference.
Samadani, M., Kwuimy, C. K., & Nataraj, C. (2015). Modelbased fault diagnostics of nonlinear systems using the features of the phase space response. Communications in Nonlinear Science and Numerical Simulation, 20(2), 583–593.
Serridge, M. (1990). Ten crucial concepts behind trustworthy fault detection in machine condition monitoring. In J. M. Montalv˜ao e Silva & F. A. Pina da Silva (Eds.), Vibration and wear in high speed rotating machinery (pp. 729–740). Dordrecht: Springer Netherlands.
Syta, A., Jonak, J., Jedlinski, L., & Litak, G. (2012). Failure diagnosis of a gear box by recurrences. Journal of Vibration and Acoustics, 134, 041006.
Vachtsevanos, G., Lewis, F., Roemer, M., Hess, A., &Wu, B. (Eds.). (2006). Intelligent fault diagnosis and prognosis for engineering systems. Wiley. Wang, W. J., & McFadden, P. D. (1996, May). Application ofWavelets to Gearbox Vibration Signals for Fault Detection. Journal of Sound Vibration, 192, 927-939. doi: 10.1006/jsvi.1996.0226
Wang, W. Q., Ismail, F., & Farid Golnaraghi, M. (2001, September). Assessment of Gear Damage Monitoring Techniques Using Vibration Measurements. Mechanical Systems and Signal Processing, 15, 905-922. doi: 10.1006/mssp.2001.1392
Yuan, J., He, Z., & Zi, Y. (2010). Gear fault detection using customized multiwavelet lifting schemes. Mechanical Systems and Signal Processing, 24(5), 1509 - 1528. (Special Issue: Operational Modal Analysis) doi: https://doi.org/10.1016/j.ymssp.2009.11.003
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