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基于集合经验模态分解和瞬时半周期模型的应力波信号去噪。

Stress wave signal denoising using ensemble empirical mode decomposition and an instantaneous half period model.

机构信息

School of Information Engineering, Zhejiang A & F University, Hangzhou, Zhejiang, 311300, China.

出版信息

Sensors (Basel). 2011;11(8):7554-67. doi: 10.3390/s110807554. Epub 2011 Aug 2.

DOI:10.3390/s110807554
PMID:22164032
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3231701/
Abstract

Stress-wave-based techniques have been proven to be an accurate nondestructive test means for determining the quality of wood based materials and they been widely used for this purpose. However, the results are usually inconsistent, partially due to the significant difficulties in processing the nonlinear, non-stationary stress wave signals which are often corrupted by noise. In this paper, an ensemble empirical mode decomposition (EEMD) based approach with the aim of signal denoising was proposed and applied to stress wave signals. The method defined the time interval between two adjacent zero-crossings within the intrinsic mode function (IMF) as the instantaneous half period (IHP) and used it as a criterion to detect and classify the noise oscillations. The waveform between the two adjacent zero-crossings was retained when the IHP was larger than the predefined threshold, whereas the waveforms with smaller IHP were set to zero. Finally the estimated signal was obtained by reconstructing the processed IMFs. The details of threshold choosing rules were also discussed in the paper. Additive Gaussian white noise was embedded into real stress wave signals to test the proposed method. Butterworth low pass filter, EEMD-based low pass filter and EEMD-based thresholding filter were used to compare filtering performance. Mean square error between clean and filtered stress waves was used as filtering performance indexes. The results demonstrated the excellent efficiency of the proposed method.

摘要

基于应力波的技术已被证明是一种准确的无损检测手段,可用于确定木质材料的质量,并已广泛用于此目的。然而,结果通常不一致,部分原因是处理非线性、非平稳的应力波信号存在很大困难,这些信号通常会受到噪声的干扰。本文提出了一种基于集合经验模态分解(EEMD)的信号去噪方法,并将其应用于应力波信号。该方法将固有模态函数(IMF)中两个相邻过零点之间的时间间隔定义为瞬时半周期(IHP),并将其用作检测和分类噪声振荡的标准。当 IHP 大于预定义的阈值时,保留两个相邻过零点之间的波形,而 IHP 较小的波形则设置为零。最后,通过对处理后的 IMF 进行重构,得到估计信号。本文还讨论了阈值选择规则的细节。将加性高斯白噪声嵌入到真实的应力波信号中,以测试所提出的方法。采用巴特沃斯低通滤波器、基于 EEMD 的低通滤波器和基于 EEMD 的阈值滤波器进行滤波性能比较。干净和滤波后的应力波之间的均方误差用作滤波性能指标。结果表明了该方法的优异效率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17b6/3231701/5681b8f1c155/sensors-11-07554f10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17b6/3231701/a84da69a80ab/sensors-11-07554f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17b6/3231701/0e403a798e5f/sensors-11-07554f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17b6/3231701/a677f97a6624/sensors-11-07554f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17b6/3231701/36a893e2d3bb/sensors-11-07554f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17b6/3231701/1b4e6a6ba43e/sensors-11-07554f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17b6/3231701/ad72e3f67028/sensors-11-07554f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17b6/3231701/2f3d550d3b56/sensors-11-07554f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17b6/3231701/dafe07114152/sensors-11-07554f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17b6/3231701/bc3d0f764cb0/sensors-11-07554f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17b6/3231701/5681b8f1c155/sensors-11-07554f10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17b6/3231701/a84da69a80ab/sensors-11-07554f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17b6/3231701/0e403a798e5f/sensors-11-07554f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17b6/3231701/a677f97a6624/sensors-11-07554f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17b6/3231701/36a893e2d3bb/sensors-11-07554f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17b6/3231701/1b4e6a6ba43e/sensors-11-07554f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17b6/3231701/ad72e3f67028/sensors-11-07554f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17b6/3231701/2f3d550d3b56/sensors-11-07554f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17b6/3231701/dafe07114152/sensors-11-07554f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17b6/3231701/bc3d0f764cb0/sensors-11-07554f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/17b6/3231701/5681b8f1c155/sensors-11-07554f10.jpg

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