Adaptive Spectral Subtraction Noise Reduction Method for Generalized Acoustic Emission Signals of Fatigue Cracks in Wind Turbine Towers

PU Zhaowei; YAN Zhaoli; LIU Yongqian

doi:10.19805/j.cnki.jcspe.2026.250123

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Adaptive Spectral Subtraction Noise Reduction Method for Generalized Acoustic Emission Signals of Fatigue Cracks in Wind Turbine Towers

更新时间：2026-06-11

- Adaptive Spectral Subtraction Noise Reduction Method for Generalized Acoustic Emission Signals of Fatigue Cracks in Wind Turbine Towers
- Chinese Journal of Power Engineering Vol. 46, Issue 6, Pages: 140-148(2026)
- 作者机构：
  
  1. 华北电力大学新能源学院,北京,102206
  2. 北京化工大学机电工程学院,北京,100029
- 作者简介：
- 基金信息：
- DOI：10.19805/j.cnki.jcspe.2026.250123
  CLC： TB551
- Published：2026
- 稿件说明：
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普赵伟, 阎兆立, 刘永前. 风电塔筒疲劳裂纹声发射信号分频自适应谱减降噪方法[J]. 动力工程学报, 2026, 46(6): 140-148.
普赵伟, 阎兆立, 刘永前. 风电塔筒疲劳裂纹声发射信号分频自适应谱减降噪方法[J]. 动力工程学报, 2026, 46(6): 140-148. DOI： 10.19805/j.cnki.jcspe.2026.250123.

DOI： 10.19805/j.cnki.jcspe.2026.250123

摘要

声发射技术作为新型风电塔筒裂纹检测手段

现场应用中常受噪声干扰

需要采取适当方法减少噪声影响。基于裂纹声发射信号特点

提出了一种分频自适应的谱减滤波器估计方法

放宽了原谱减法适用条件

无需检测有效信号便能在复杂噪声环境对裂纹声发射信号降噪。该方法通过动态均值估计得到不同频带谱减滤波器

再加权相加得到新的谱减滤波器。通过疲劳试验中采集到的声学数据验证降噪效果

结果表明该方法相比于小波阈值法和传统经验模态分解(EMD)降噪效果更好

信号经过降噪后的疲劳裂纹识别准确率提升至97.6%

有助于裂纹信号的准确识别。

Abstract

As a novel means for crack detection in wind turbine towers

acoustic emission technology is often subject to noise interference during field applications

so appropriate methods are required to mitigate the impact of noise. Based on the characteristics of crack acoustic emission signals

a frequency-division adaptive spectral subtraction filter estimation method was proposed. This method expands the applicable conditions of the original spectral subtraction method

allowing for the noise reduction of crack acoustic emission signals in complex noise environments without the necessity of detecting effective signals. In this method

spectral subtraction filters for different frequency bands were obtained through dynamic mean estimation

and then weighted and summed to generate a new spectral subtraction filter. The noise reduction effect was validated using acoustic data collected from fatigue tests. Results demonstrate that this method is superior to wavelet thresholding method and traditional empirical mode decomposition (EMD) in terms of noise reduction. The accuracy of fatigue crack identification after noise reduction has been improved to 97.6%

aiding in the accurate identification of crack signals.

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references

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