采用小波分析的风力机叶片声发射信号处理与分析

官宇楠; 王超; 宋博扬

doi:10.7652/xjtuxb202505020

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采用小波分析的风力机叶片声发射信号处理与分析

—————————————————————— | 更新时间：2025-08-26

- 采用小波分析的风力机叶片声发射信号处理与分析
- Processing and Analysis of Acoustic Emission Signals from Wind Turbine Blades Using Wavelet Analysis
- 西安交通大学学报 2025年59卷第5期页码：209-216
- 作者机构：
  
  1.西北工业大学航海学院， 710072，西安
  2.中国船舶集团有限公司， 200011，上海
- 作者简介：
  
  官宇楠（2002—），男，本科生；
  王超（通信作者），男，讲师。
- 基金信息：
  
  陕西省重点研发计划资助项目(2021GXLH-01-06)
- DOI：10.7652/xjtuxb202505020
  中图分类号： TM315
- 收稿：2024-07-18，
  
  网络首发：2024-10-22，
  
  纸质出版：2025-05-10
- 稿件说明：
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官宇楠, 王超, 宋博扬. 采用小波分析的风力机叶片声发射信号处理与分析[J]. 西安交通大学学报, 2025,59(5):209-216.

GUAN Yunan, WANG Chao, SONG Boyang. Processing and Analysis of Acoustic Emission Signals from Wind Turbine Blades Using Wavelet Analysis[J]. Journal of Xi’an Jiaotong University, 2025, 59(5): 209-216.
官宇楠, 王超, 宋博扬. 采用小波分析的风力机叶片声发射信号处理与分析[J]. 西安交通大学学报, 2025,59(5):209-216. DOI： 10.7652/xjtuxb202505020.

GUAN Yunan, WANG Chao, SONG Boyang. Processing and Analysis of Acoustic Emission Signals from Wind Turbine Blades Using Wavelet Analysis[J]. Journal of Xi’an Jiaotong University, 2025, 59(5): 209-216. DOI： 10.7652/xjtuxb202505020.

摘要

为有效对风力机叶片损伤进行在线监测，采用小波分析技术对风力机叶片运行状态下的声发射信号进行降噪和时频分析。采用多通道高频采集设备搭配声发射传感器，搭建双通道声发射信号在线检测系统，实时采集某型全尺寸叶片带伤运行声发射信号，改进了小波阈值降噪方法并对该方法在真实叶片损伤定位和模态分析中的表现进行检验。实验结果表明：与传统的维纳滤波降噪、谱减法降噪等方法相比，改进的小波阈值降噪方法应用于在线监测系统时具有精度高、速度快、稳定性好的优势：对模拟损伤的声发射信号进行时差定位，精度达到2.2%；同时，采用改进的小波分析也能够从风力机叶片的声发射信号中准确检出叶片的运行模态信息。改进的小波阈值降噪在风力机叶片损伤定位和模态信息检测方面表现良好，具备实际的应用价值。

Abstract

To effectively conduct online monitoring of the damage to wind turbine blades

wavelet analysis technology was adopted to reduce noise and perform time-frequency analysis on the acoustic emission signals of wind turbine blades during operation. A dual-channel acoustic emission signal online detection system consisting of a multi-channel high-frequency acquisition device and several domestic acoustic emission sensors was established to collect real-time acoustic emission signals from a certain type of full-scale blade operating with damage. The wavelet threshold denoising method was optimized and its performance in the localization and operation modal analysis of actual blade damage was also examined. As revealed by experimental results

in contrast to traditional methods such as Wiener filtering denoising and spectral subtraction denoising

the optimized wavelet threshold denoising method features high precision

fast speed

and good stability when applied to the online monitoring system: The time difference positioning accuracy for the acoustic emission signals of simulated damage reaches 2.2%; wavelet analysis can also effectively detect the operation modal information of the blades. The experiment reveals that wavelet threshold denoising performs well in the aspects of damage localization and modal information detection of wind turbine blades and holds practical application value.

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Keywords

references

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