Abstract
Higher order spectral analysis of vibration signals is an efficient tool in condition monitoring and fault detection and diagnosis of rotating machinery. In this paper, features extracted from vibration bispectrum are used in fault classification of critical rotating components in the AH-64D helicopter tail rotor drive train system. Different classifiers are used to compare the performance of the proposed algorithm based on bispectrum to the traditional algorithms based on linear auto- and cross-power spectral analysis techniques. Principal component analysis (PCA) is used to reduce the size of features extracted from vibration bispectrum and linear spectral analysis, then the reduced set is used to train different classifiers. Using different criteria such as accuracy, precision, sensitivity, F score, true alarm, and error classification accuracy (ECA), the performance of the proposed algorithm is evaluated and compared against similar classification algorithms. The proposed method is verified using real-world data collected from a dedicated AH-64D helicopter drive-train research test bed at the CPM center, University of South Carolina. The proposed algorithm increases the accuracy of fault detection to 96.88%, precession to 95.83%, sensitivity to 95.83%.
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References
Jardine, A. K., Lin, D., & Banjevic, D., 2006. “A review on machinery diagnostics and prognostics implementing condition-based maintenance” Mechanical Systems and Signal Processing, 20(7), 1483-1510.
Samuel, P. D., & Pines, D. J., 2005. “A review of vibration-based techniques for helicopter transmission diagnostics” Journal of Sound and Vibration, 282(1-2), 475-508.
Kang, P., & Birtwhistle, D., 1998.“Analysis of vibration signals for condition monitoring of power switching equipment using wavelet transform,” in Proc IEEE-SP Int. Symp. Time Frequency and Time Scale Analysis, pp. 6–9.
Baydar, N., & Ball, A., 2003. “Detection of gear failures via vibration and acoustic signals using wavelet transform,” Mechanical Systems and Signal Processing, 17(4), 787-804.
Grabill, P., Seale, J., & Brotherton, T., 2002. “ATEDS: Airborne turbine engine diagnostic system,” 2004 IEEE Aerospace Conference Proceedings (IEEE Cat. No.04TH8720).
Hassan, M. A., Tarbutton, J., Bayoumi, A., & Shin, Y, 2014. “Condition monitoring of helicopter drive shafts using quadratic-nonlinearity metric based on cross-bispectrum,” IEEE Transactions on Aerospace and Electronic Systems, 50(4), 2819-2829.
Rivera, I., Ramirez, A., & Rodriguez, D, 2005. “A time-frequency signal analysis system for power quality assessment,” 48th Midwest Symposium on Circuits and Systems, 2005., 2, 1670-1680.
Hassan, M. A., Bayoumi, A. E., & Shin, Y, 2014. “Quadratic-nonlinearity index based on bicoherence and its application in condition monitoring of drive-train components,” IEEE Transactions on Instrumentation and Measurement, 63(3), 719-728.
Proakis,J. G., and Manolakis,D. G, 2007. “Power spectrum estimation,” in Digital Signal Proccessing: Principles, Algorithms, and Applications, 4th ed. New Jersey: Prentice Hall, pp. 960-1040.
Kim, Y. C., & Powers, E. J., 1979. “Digital bispectral analysis and its applications to nonlinear wave interactions,” IEEE Trans. Plasma Sci. IEEE Transactions on Plasma Science, 7(2), 120-131.
Bishop, Christopher M.,2006 “Pattern recognition and machine learning”. New York: Springer.
Ghaderi, A., Mohammadpour, H. A., Ginn, H., & Shin, Y, 2015. “High impedance fault detection in distribution network using time-frequency based algorithm,” 2015 IEEE Power & Energy Society General Meeting.
Hotelling, H.,1933.” Analysis of a complex of statistical variables into principal components,” Baltimore: Warwick & York.
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Habib, M.R., Hassan, M.A., Abul Seoud, R.A., Bayoumi, A.M. (2017). Mechanical fault detection and classification using pattern recognition based on bispectrum algorithm. In: Bahei-El-Din, Y., Hassan, M. (eds) Advanced Technologies for Sustainable Systems. Lecture Notes in Networks and Systems, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-48725-0_15
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DOI: https://doi.org/10.1007/978-3-319-48725-0_15
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