基于小波包特征提取及支持向量回归机的光纤布拉格光栅冲击定位系统

芦吉云; 王帮峰; 梁大开

doi:10.3788/OPE.20122004.0712

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基于小波包特征提取及支持向量回归机的光纤布拉格光栅冲击定位系统

现代应用光学 | 更新时间：2020-08-12

- 基于小波包特征提取及支持向量回归机的光纤布拉格光栅冲击定位系统
- Identification of impact location by using FBG based on wavelet packet feature extraction and SVR
- 光学精密工程 2012年20卷第4期页码：712-718
- 作者机构：
  
  1. 南京航空航天大学民航学院,江苏南京,210016
  2. 南京航空航天大学航空宇航学院,江苏南京,210016
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目(No.51075207)();江苏省博士后基金资助项目(No.1001014C)();中国博士后基金资助项目(No.20110491414)(
- DOI：10.3788/OPE.20122004.0712
  中图分类号： V251.2;TN253
- 收稿日期：2011-10-18，
  
  修回日期：2011-12-01，
  
  网络出版日期：2012-04-22，
  
  纸质出版日期：2012-04-22
- 稿件说明：
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芦吉云, 王帮峰, 梁大开. 基于小波包特征提取及支持向量回归机的光纤布拉格光栅冲击定位系统[J]. 光学精密工程, 2012,(4): 712-718

LU Ji-yun, WANG Bang-feng, LIANG Da-kai. Identification of impact location by using FBG based on wavelet packet feature extraction and SVR[J]. Editorial Office of Optics and Precision Engineering, 2012,(4): 712-718
芦吉云, 王帮峰, 梁大开. 基于小波包特征提取及支持向量回归机的光纤布拉格光栅冲击定位系统[J]. 光学精密工程, 2012,(4): 712-718 DOI： 10.3788/OPE.20122004.0712.

LU Ji-yun, WANG Bang-feng, LIANG Da-kai. Identification of impact location by using FBG based on wavelet packet feature extraction and SVR[J]. Editorial Office of Optics and Precision Engineering, 2012,(4): 712-718 DOI： 10.3788/OPE.20122004.0712.

摘要

以光纤布拉格光栅(FBG)为传感网络

构建了复合材料冲击载荷实时在线监测系统

研究了基于小波包特征提取及支持向量回归机的光纤-碳纤维复合材料结构冲击定位方法。针对同一冲击点

分析不同传感信号

获得了冲击响应信号小波包能量谱

分析结果表明小波包能量谱中特定阶数对冲击敏感。改变冲击点位置研究小波包能量谱与冲击位置之间的关系

提出将第6阶小波包能量值作为冲击定位的特征向量。采用支持向量回归机建立样本数据的回归模型

预测冲击载荷位置

并对支持向量机的相关调整参数进行了优化。实验表明

支持向量机的网络测试误差为4.81%。研究结果可为碳纤维复合材料(CFRP)层状结构的冲击性能评估提供可行的实验方法。

Abstract

A real-time monitoring system of composite impact loads was constructed by a Fiber Bragg Grating(FBG) sensor network

and the wavelet packet feature extraction and a Support Vector Regression(SVR) were used to identify the impact location. For the impact response signals at the same position measured by different FBG sensors

the wavelet packet energy spectrum analysis shows that some specifically frequency bands of sensor signals are sensitive to the impact. The relation between impact location and wavelet energy was studied and the sixth decomposition level wavelet packet energy was chosen as the characteristic vector of the impact location. The SVR whose tuning parameters have been optimized was used to established the sample regression model and predict the impact location. The result shows that network testing error of the SVR is 4.81%. The research provides a practical reference for the impact performance evaluation of the structures from carbon fiber reinforced plastics.

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references

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