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基于提升小波包的集合经验模态分解方法的电磁超声诊断

Electromagnetic ultrasonic diagnosis based on lifting wavelet packet and set empirical mode decomposition

Advance in Modern Physics / 2020,2(2): 43-51 / 2020-05-19 2311 3626

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• Authors: 曹江英
• Information:

  扬州大学，扬州

• Keywords:
  Electromagnetic acoustic; Lifting wavelet packet; Ensemble empirical mode decomposition( EEMD); Non-destructive inspection; Signal-to-noise ratio( SNR)
  电磁超声; 提升小波包; 集合经验模态分解（EEMD）; 无损检测; 信 噪比（SNR）
• Abstract: The electromagnetic acoustic detection features small echo signal and low signal-to-noise ratio. In order to effectively extract the implicit defect feature in unstable signal, the diagnosis method of ensemble empirical mode decomposition( EEMD) based on lifting wavelet packet is proposed. Firstly, the influence of high frequency noise on EEMD is removed by using lifting wavelet packet transform algorithm with improved threshold function and selecting optimal threshold; then, the de-noised signal is decomposed with EEMD, the echo signal is analyzed in time and frequency domains. The results show that the method of EEMD based on lifting wavelet packet can remove the noise of the echo signal effectively and extract the malfunction feature in the low SN R signal. This method provides reliable basis for defect diagnosis. 电磁超声检测的回波信号幅值小，信噪比低。为有效提取非平稳信号 中隐含的缺陷特征，提出了基于提升小波包的集合经验模态分解（EEMD）诊断 方法。首先应用改进阈值函数的提升小波包变换算法，选取最优阈值方法去除 高频噪声对 EEMD 的影响；然后对降噪信号进行 EEMD 分解，对回波信号进行 时频分析与诊断。结果表明，基于提升小波包的 EEMD 分析方法可有效去除回 波信号噪声，提取低信噪比信号的故障特征，为缺陷诊断提供可靠依据。
• DOI: https://doi.org/10.35534/amp.0202007c
• Cite: 曹江英．基于提升小波包的集合经验模态分解方法的电磁超声诊断［J］．现代物理进展，2020，2（2）：43-51．