Abstract
To overcome the inherent deficiencies of conventional time-frequency analysis (TFA) methods, i.e., different TFA methods or the same TFA method with different control parameters will present different results for the same target signal, a novel scheme named as the time-frequency data fusion (TFDF) is developed in this study by extending the idea of data fusion technique. The TFDF technique can present a more accurate time-frequency presentation for the target signal than what can be achieved by any individual TFA method. Therefore, the TFDF has potential to render a significantly improved time-frequency representation and greatly facilitates extracting time-frequency features of target signals. This will promote the applications of TFA in engineering practices and make TFA methods more acceptable to field engineers. The effectiveness of the TFDF technique is validated by three numerical case studies and the analysis of a rubbing-impact signal collected from a rotor test rig.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Jardine, A.K.S., Lin, D., Banjev, D.: A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech. Syst. Signal Process., 1483–1510 (2006)
Peng, Z.K., Chu, F.L.: Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mech. Syst. Signal Process., 199–221 (2004)
Huang, N.E., Shen, Z., Long, S.R.: A new view of nonlinear water waves: the Hilbert spectrum. Annu. Rev. Fluid Mech., 417–457 (1999)
Wang, C.T., Gao, R.X., Yan, R.Q.: Unified time–scale–frequency analysis for machine defect signature extraction: theoretical framework. Mech. Syst. Signal Process. Mech. Syst. Signal Process., 226–235 (2009)
Geng, Z.M., Chen, J., Hull, J.B.: Analysis of engine vibration and design of an applicable diagnosing approach. Int. J. Mech. Sci., 1391–1410 (2003)
Teich, M.C., Heneghan, C., Khanna, S.M., et al.: Investigating routers to chaos in the guinea-pig cochlea using the continuous wavelet transform and the short-time Fourier-transform. Ann. Biomed. Eng., 583–607 (1995)
Conn, T., Hamilton, J.: Time–frequency analysis of time-varying spectra with application to rotorcraft testing. IEEE Antennas Propag. Mag., 148–153 (2005)
Lebaroud, A., Clerc, G.: Accurate diagnosis of induction machine faults using optimal time–frequency representations. Eng. Appl. Artif. Intell., 815–822 (2009)
Padovese, L.R.: Hybrid time–frequency methods for non-stationary mechanical signal analysis. Mech. Syst. Signal Process., 1047–1064 (2004)
Leon, C.: Time Frequency Analysis. Prentice Hall, Hunter College, New York (1995)
Nawab, S.H., Quatieri, T.: Short-time Fourier transform. In: Nawab, S.H., Quatieri, T. (eds.) Adv. Top. Signal Process. Prentice-Hall, Englewood Cliffs (1988)
Hlawatsch, F., Manickam, T.G., Urbanke, R.L., Jones, W.: Smoothed pseudo-Wigner distribution, Choi–Williams distribution, and cone-kernel representation: ambiguity-domain analysis and experimental comparison. Signal Process., 149–168 (1995)
Rao, R.M., Bopardikar, A.S.: Wavelet Transforms-Introduction to Theory and Applications. Addison Wesley Longman, Reading (1998)
Peng, Z.K., Chu, F.L., Tse, P.: Detection of the rubbing caused impacts for rotor-stator fault diagnosis using reassigned scalogram. Mech. Syst. Signal Process., 391–409 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hu, L., Chen, B., Huang, Z. (2012). A New Method for Vibration Signal Analysis Using Time-Frequency Data Fusion Technique. In: Wang, Y., Zhang, X. (eds) Internet of Things. Communications in Computer and Information Science, vol 312. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32427-7_53
Download citation
DOI: https://doi.org/10.1007/978-3-642-32427-7_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32426-0
Online ISBN: 978-3-642-32427-7
eBook Packages: Computer ScienceComputer Science (R0)