Abstract
A time-frequency demodulation technique based on local mean decomposition (LMD) is proposed for rotating machine diagnosis. In addition, methods for boundary processing and for determining the step size of the moving average are presented to improve LMD algorithm. Instantaneous amplitude (IA) and instantaneous frequency (IF) of the signal can be achieved using the improved LMD method. A well-constructed description of the derived IAs and IFs is represented in the form of instantaneous time-frequency spectrum (ITFS), which preserves both the time and frequency information simultaneously. Results of three synthetic signals indicate that the proposed method is much better in extracting the comprehensive carrier and modulated components, compared with Hilbert-Huang transform and stationary wavelet transform. The validity of the technique is further demonstrated on the rotor system and a gearbox. The transient fluctuations of the IF and the impulsive signatures can be successfully identified in the ITFS. Moreover, it has been demonstrated that the proposed time-frequency demodulation technique is much more effective and sensitive than the other methods in detecting impulsive and modulated components.
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References
Maragos P., Kaiser J.F., Quatieri T.F., “On amplitude and frequecy demodulation using energy operator”, IEEE Transactions on Signal Processing, 1993, 41:1532–1550.
Potamianos A., Maragos P., A comparison of the energy operator and the Hilbert transform approach to signal and speech demodulation, Signal Processing, 1994, 37: 95–120.
Bovik A.C., Maragos P., Quatieri, T.F., “AM-FM energy detection and separation in noise using multiband energy operators”, IEEE Transactions on Signal Processing, 1993, 41:3245–3265.
Santhanam B., Maragos P., “Multicomponent AM-FM demodulation via periodicity-based algebraic separation and energy-based demodulation”, IEEE Transactions on Communications, 2000, 48:473–490.
Gianfellci F., Biagetti G., Crippa P., Turchetti C., “Multicomponent AM-FM representations: an asymptotically exact approach,” IEEE Trans. on Audio, Speech, and Language Processing, 2007, 15:823–837.
Qin Y., Qin S., Mao Y., “Research on iterated Hilbert transform and its application in mechanical fault diagnosis,” Mechanical Systems and Signal Processing, 2008, 22(8): 1967–1980.
Nikolaou N.G., Antoniadis I.A., “Demodulation of vibration signals generated by defects in rolling element bearings using complex shifted Morlet wavelets,” Mechanical Systems and Signal Processing, 2002, 16:677–694.
Cui S.., Li X.L., Ouyang G.X., Guan X.P., “Detection of epileptic spikes with empirical mode decomposition and nonlinear energy operator,” Advances in Neural Networks, vol. 3498, ed. Berlin: Springer-Verlag Berlin, 2005, pp. 445–450.
Cheng J.S., Yu D. J., Yang Y., “The application of energy operator demodulation approach based on EMD in machinery fault diagnosis,” Mechanical Systems and Signal Processing, 2007, 21:668–677.
Liu X. Q., “Hybrid wavelet packet-teager energy operator analysis and its application for gearbox fault diagnosis”, Chinese Journal of Mechanical Engineering, 2007, 20(6): 79–83.
He Q., Wang, X., “Time-frequency manifold correlation matching for periodic fault identification in rotating machines,” Journal of Sound and Vibration, 2013, 332:2611–2626.
Smith J.S., “The local mean decomposition and its application to EEG perception data,” Journal of the Royal Society Interface, 2005, 2(5):443–454.
Picinbono B., “On instantaneous amplitude and phase of signals,” IEEE Transactions on Signal Processing, 1997, 45:552–560.
Huang N.E., Shen Z., Long S. R., “The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 1998, 454:903–995.
Wang Y., He Z., Zi Y., “A demodulation method based on local mean decomposition and its application in rub-impact fault diagnosis,” Measurement Science & Technology, 2009, 20, p. (10 pages).
Huang N.E., Shen Z., Long S. R., “A new view of nonlinear water waves: the Hilbert spectrum,” Annual Review of Fluid Mechanics, 1999, 31:417–457.
Olhede S., Walden A.T., “The Hilbert spectrum via wavelet projections,” Proceedings of the Royal Society of London Series A-Mathematical Physical and Engineering Sciences, 2004, 460:955–975.
Kay S., “Fast and accurate single frequency estimator,” IEEE Transactions on Acoustics, Speech, and Signal Processing, 1989, 37:1987–1990.
Acknowledgements
The financial sponsorship from the project of National Natural Science Foundation of China (51475098 and 61463010) and Guangxi Natural Science Foundation (2016GXNSFFA380008) are gratefully acknowledged. It’s also sponsored by Guangxi Experiment Center of Information Science (20130312) and Guangxi Key Laboratory of Manufacturing System and Advanced Manufacturing Technology (15-140-30-001Z).
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Wang, Y., Chen, X., Zi, Y. (2017). Time-Frequency Demodulation Analysis Based on LMD and Its Applications. In: Yan, R., Chen, X., Mukhopadhyay, S. (eds) Structural Health Monitoring. Smart Sensors, Measurement and Instrumentation, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-319-56126-4_12
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DOI: https://doi.org/10.1007/978-3-319-56126-4_12
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