Feature Extraction of Early AE Signal Based on the Lifting Wavelet and Empirical Mode Decomposition

Abstract:

To solve the problem of difficult to extract fault features from the early weak AE signal, a method combining lifting wavelet with EMD is proposed. Firstly, the lifting wavelet is used to de-noise the AE signal, then the signal after de-noising is decomposed by EMD, and gets all kinds of different frequency IMFS. Finally, the most relevant IMF with the original signal depends on the size of the correlation coefficients, and the fault characteristic signal is extracted via combining time- frequency analysis and envelope spectrum analysis. The result of simulation and test signal shows that this method can effectively extracts the failure characteristics from early AE signal.

• Periodical: Advanced Engineering Forum (Volumes 2-3)
• Pages: 193-199
• DOI: https://doi.org/10.4028/www.scientific.net/AEF.2-3.193
• Online since: December 2011
• Authors: Xue Jun Li, Zong Qun Deng, Ling Li Jiang, Kuan Fang He
• Keywords: Empirical Mode Decomposition (EMD), Envelope Analysis, Feature Extraction, Lifting Wavelet

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