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Two Stage Helical Gearbox Fault Detection and Diagnosis based on Continuous Wavelet Transformation of Time Synchronous Averaged Vibration Signals

Elbarghathi, Fathalla, Wang, T., Zhen, Dong, Gu, Fengshou and Ball, Andrew (2012) Two Stage Helical Gearbox Fault Detection and Diagnosis based on Continuous Wavelet Transformation of Time Synchronous Averaged Vibration Signals. Journal of Physics: Conference Series, 364. 012083. ISSN 1742-6596

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    Abstract

    Vibration signals from a gearbox are usually very noisy which makes it difficult to find reliable symptoms of a fault in a multistage gearbox. This paper explores the use of time synchronous average (TSA) to suppress the noise and Continue Wavelet Transformation (CWT) to enhance the non-stationary nature of fault signal for more accurate fault diagnosis. The results obtained in diagnosis an incipient gear breakage show that fault diagnosis results can be improved by using an appropriate wavelet. Moreover, a new scheme based on the level of wavelet coefficient amplitudes of baseline data alone, without faulty data samples, is suggested to select an optimal wavelet.

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    Item Type: Article
    Subjects: T Technology > TJ Mechanical engineering and machinery
    Schools: School of Computing and Engineering
    School of Computing and Engineering > Automotive Engineering Research Group
    School of Computing and Engineering > Diagnostic Engineering Research Centre
    School of Computing and Engineering > Diagnostic Engineering Research Centre > Energy, Emissions and the Environment Research Group
    School of Computing and Engineering > Diagnostic Engineering Research Centre > Machinery Condition and Performance Monitoring Research Group
    School of Computing and Engineering > Diagnostic Engineering Research Centre > Measurement System and Signal Processing Research Group
    School of Computing and Engineering > High-Performance Intelligent Computing
    School of Computing and Engineering > High-Performance Intelligent Computing > Information and Systems Engineering Group
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    References:

    [1] D. N. Chorafas, 1990. Knowledge Engineering, Van Nostrand Reinhold, first edition.
    [2] J. D. Simth 1983 Gears and their Vibration: A Basic Approach to understand Gears Noise, NY: Marcel Dekker, Inc., ISBN: 082471797X.
    [3] F. Elbarghathi, et al., 2012. Two stage helical gearbox fault detection and diagnosis based on continuous wavelet transformation of time synchronous average vibration signals, Journal of Physics Conferences series, 364.012083.ISSN 1742-6596.
    [4] G. Dapliaz, A. R. Rubini, 2000. Effectiveness and sensitivity of vibration processing techniques for local fault detection in gears, Mechanical System and Signal Processing, 14(3), pp. 387-412.
    [5] P. W. Stevens, et al., 1996. A multidisciplinary research approach to rotorcraft health and usage monitoring, American Helicopter Society 52nd Annual Forum, Washington .D.C., pp. 1732-1751.
    [6] R. B. Randall, 1982. A new method of modelling gear fault, Journal of Mechanical Design, 104, pp. 259-267
    [7] F. Combet, L. Gelman 2007. An automated methodology for performing time synchronous averaging of a gearbox signal without speed sensor, Mechanical Systems and Signal Processing, 21. pp. 2590-2606.
    [8] L. Cohen, 1995. Time-Frequency Analysis, NJ:Prentice-Hall.
    [9] W. J. Staszewski, A. C. Robertson, 2007. Time-frequency and time-scale analysis for structural health monitoring, Philosophycal TransSactions of Rayal Society, 365 (1851), pp 449-477.
    [10] J. Ville, 1948. Theories et application de la notion de signal analytique, Cables et Transmission, A(1), pp. 61-74
    [11] B. B. Hubbard, 1998. The world According to wavelet, MA wellesley .
    [12] J. Yonghua et al., 2011. Feature extraction method of wind turbine based on adaptive Morlet wavelet and SVD, Renewable energy, 36, pp. 2146-2153.
    [13] H. X. Chen, et al 2005. Adaptive wavelet transform for vibration signal modelling and application in fault diagnosis of water hydraulic motor, Mechanical System and Signal Processing, 20 (2006) pp 2022-2045.
    [14] F. Combet, L. Gelman, 2007. An automated methodology for performing time synchronous averaging of a gearbox signal without speed sensor, Mechanical Systems and Signal Processing, 21, pp. 2590.
    [15] W. Wentao, L. Jing, S. Han, X. Ding, 2009. Time domain averaging based on fractional delay filter, Mechanical Systems and Signal Processing, 23(5), pp. 1447-1457.
    [16] Y. Xingjia et al., 2009. Wind turbine gearbox fault diagnosis using adaptive Morlet wavelet spectrum, The 2nd International Conference on Intelligent Computation Technology and Automation.
    [17] Cui Houxi, et al., 2009. Research on fault daignosis for reciorocating compressor valave using information entropy and SVD method, Journal of loss prevention in the process industries 22 (2009) pp. 864-867.

    Depositing User: Cherry Edmunds
    Date Deposited: 11 Jul 2012 10:43
    Last Modified: 04 Jun 2013 10:56
    URI: http://eprints.hud.ac.uk/id/eprint/14195

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