采用改进的希尔伯特黄变换的损伤检测特征提取方法

张涛; 丁碧云; 赵鑫

doi:10.7652/xjtuxb201810003

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采用改进的希尔伯特黄变换的损伤检测特征提取方法

更新时间：2025-07-09

- 采用改进的希尔伯特黄变换的损伤检测特征提取方法
- A Feature Extraction Method of Defect Detection Using Improved Hilbert-Huang Transform
- 西安交通大学学报 2018年52卷第10期页码：16-23
- 作者机构：
  
  天津大学电气自动化与信息工程学院,天津,300072
- 作者简介：
- 基金信息：
  
  : 2018-03-25。作者简介: 张涛(1975-),男,副教授。基金项目: 国家自然科学基金资助项目(61574099,61350009)
- DOI：10.7652/xjtuxb201810003
  中图分类号： O235;TP391.4
- 网络首发：2018-10-10，
  
  纸质出版：2018
- 稿件说明：
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张涛, 丁碧云, 赵鑫. 采用改进的希尔伯特黄变换的损伤检测特征提取方法[J]. 西安交通大学学报, 2018,52(10):16-23.

A Feature Extraction Method of Defect Detection Using Improved Hilbert-Huang Transform[J]. 2018, 52(10): 16-23.
张涛, 丁碧云, 赵鑫. 采用改进的希尔伯特黄变换的损伤检测特征提取方法[J]. 西安交通大学学报, 2018,52(10):16-23. DOI： 10.7652/xjtuxb201810003.

A Feature Extraction Method of Defect Detection Using Improved Hilbert-Huang Transform[J]. 2018, 52(10): 16-23. DOI： 10.7652/xjtuxb201810003.

摘要

针对损伤检测中存在的难以提取准确描述损伤信息特征的问题

提出了一种基于改进的希尔伯特黄变换的有效特征选择方法。该方法首先对信号进行小波包分解和重构

得到一系列窄带信号

然后对其分别做经验模式分解得到若干个固有模式函数

基于互信息量筛选出真实固有模式函数

并对真实固有模式函数分别做希尔伯特变换得到瞬时属性

最后根据瞬时属性提取相关的时频特征。在损伤检测应用中

采用前向反馈神经网络对提取的特征进行分类

结果表明

利用该方法提取的音频信号特征非常有效

综合分类性能指标F相比于原始的希尔伯特黄变换提高了7.7%。

Abstract

An effective feature selection method based on an improved Hilbert-Huang transform is proposed to focus on the problem that it is difficult to extract the characteristic features of the defect information in acoustic defect detection systems. Firstly

audio signals are decomposed and reconstructed by the wavelet package to obtain a series of narrow-band signals. Secondly

all of the narrow-band signals are respectively decomposed via the empirical mode decomposition method to obtain several intrinsic mode function components. Then the real intrinsic mode function components are screened out based on mutual information.Instantaneous attributes are obtained from these intrinsic mode function components through Hilbert transformation. Finally

relevant time-frequency features are extracted based on these instantaneous attributes. The extracted features are classified by a back propagation neural network in acoustic defect detection. Results show that the features of audio signals extracted by the proposed method is very effective and the comprehensive classification performance index F improves by 7.7% compared with the original Hilbert-Huang transform.

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references

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