A New Method for Vibration Signal Analysis Using Time-Frequency Data Fusion Technique

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.

[1]  Thomas F. Quatieri,et al.  Short-time Fourier transform , 1987 .

[2]  F. J. Owens,et al.  A short-time Fourier transform , 1988 .

[3]  Rüdiger L. Urbanke,et al.  Smoothed pseudo-Wigner distribution, Choi-Williams distribution, and cone-kernel representation: Ambiguity-domain analysis and experimental comparison , 1995, Signal Process..

[4]  Ajit S. Bopardikar,et al.  Wavelet transforms - introduction to theory and applications , 1998 .

[5]  N. Huang,et al.  A new view of nonlinear water waves: the Hilbert spectrum , 1999 .

[6]  Jin Chen,et al.  Analysis of engine vibration and design of an applicable diagnosing approach , 2003 .

[7]  Khaled H. Hamed,et al.  Time-frequency analysis , 2003 .

[8]  L. Padovese.  Hybrid time–frequency methods for non-stationary mechanical signal analysis , 2004 .

[9]  Fulei Chu,et al.  Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography , 2004 .

[10]  J. Hamilton,et al.  Time-frequency analysis of time-varying spectra with application to rotorcraft testing , 2005, IEEE Antennas and Propagation Magazine.

[11]  Peter W. Tse,et al.  Detection of the rubbing-caused impacts for rotor–stator fault diagnosis using reassigned scalogram , 2005 .

[12]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[13]  S. Khanna,et al.  Investigating routes to chaos in the guinea-pig cochlea using the continuous wavelet transform and the short-time Fourier transform , 1995, Annals of Biomedical Engineering.

[14]  Guy Clerc,et al.  Accurate diagnosis of induction machine faults using optimal time-frequency representations , 2009, Eng. Appl. Artif. Intell..

[15]  Robert X. Gao,et al.  Unified time–scale–frequency analysis for machine defect signature extraction: Theoretical framework , 2009 .