Revised empirical wavelet transform based on auto-regressive power spectrum and its application to the mode decomposition of deployable structure
| Autor: | Mengbo Qian, Tao Liu, Shaoze Yan, Zhijun Luo |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Acoustics and Ultrasonics
Computer science Mechanical Engineering Spectral density Wavelet transform 020206 networking & telecommunications 02 engineering and technology White noise Condensed Matter Physics 01 natural sciences Vibration symbols.namesake Noise Additive white Gaussian noise Autoregressive model Mechanics of Materials Physics::Space Physics 0103 physical sciences 0202 electrical engineering electronic engineering information engineering symbols Astrophysics::Solar and Stellar Astrophysics Deployable structure 010301 acoustics Algorithm |
| Zdroj: | Journal of Sound and Vibration. 431:70-87 |
| ISSN: | 0022-460X |
| DOI: | 10.1016/j.jsv.2018.06.001 |
| Popis: | Noise and non-stationary components may result in false boundaries when empirical wavelet transform (EWT) is applied to mode decomposition. In this paper, a revised method named AR-EWT is proposed to overcome this problem. The AR-EWT detects boundaries in the auto-regressive (AR) power spectrum using the Burg algorithm. In the AR power spectrum, white noise and non-stationary factors can be considerably suppressed; therefore, the correct boundaries of different mono-components are successfully detected in the power spectrum. Decomposition results of simulation signals indicate that signals added with white Gaussian noise and non-stationary components can be correctly decomposed using the AR-EWT, which cannot be achieved using the original EWT. Furthermore, comparative analysis with other revised methods is conducted. The results show AR-EWT has superiority over these algorithms. Finally, an application of mode decomposition to the vibration signal of a deployable structure shows that the AR-EWT is more powerful than the original EWT. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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