基于功率谱密度分析的脉冲涡流缺陷分类法

彭英; 吴应发; 邱选兵; 刘路路; 魏计林; 陈长飞

基于功率谱密度分析的脉冲涡流缺陷分类法

1.
太原科技大学应用科学学院, 太原 030024
2.
山东能源临矿集团古城煤矿, 济宁 272100

基金项目:

国家自然基金资助项目(61178067)

山西省青年科学基金资助项目(2013021004-4)

太原科技大学博士启动基金资助项目(20132011)。

详细信息

作者简介:
彭英(1979-), 女, 博士研究生, 主要从事电磁无损检测、裂纹扩展等研究工作。

中图分类号: TG115.28
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出版历程
- 收稿日期: 2014-06-24

Defect Classification by Pulsed Eddy Current Technique Based-on Power Spectral Density Analysis

摘要

摘要: 在巨磁阻脉冲涡流传感器(GMR-PEC)上实现平板导体表面和次表面裂纹缺陷以及孔缺陷进行精确分类。在频率分析基础上, 提出了一种新的缺陷特征量——涡流差分响应信号的功率谱密度。由于主成分分析具有良好的降维特性, 采用主成分分析结合线性判别分类(PCA-LDA)和贝叶斯分类(PCA-Bayes)进行缺陷的分类。结果表明, 基于新的特征量的分类方法能实现导体表面和次表面的裂纹和孔缺陷的精确分类, 在脉冲涡流自动测量领域具有潜在的意义。
- 脉冲涡流 /
- 功率谱密度分析 /
- 缺陷分类
Abstract: The main objective of this study aims to precisely classify the cracks and cavities in surface and sub-surface by using features-based giant-magneto-resistive pulsed eddy current (GMR-PEC) sensor. A new defect feature named as the power spectral density analysis of the direct differential PEC response is carried out based-on the amplitude spectrum. Principal component analysis is designed to reduce the dimensional index with the ability of supplying the lower dimensional feature. The PCA combined linear discriminatary analysis (PCA-LDA) and the Bayesian classifier (PCA-Bayes) are both applied for defect classification. Consequently, the experimental results demonstrate that the cracks and cavities in surface and sub-surface can be classified satisfactorily by the proposed methods using the new feature, which have the potential for gauging automatic in-situ inspection for PEC.
- Pulsed eddy current /
- Power spectrum density analysis /
- Defect classification

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参考文献(18)

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基于功率谱密度分析的脉冲涡流缺陷分类法

作者简介: 彭英(1979-), 女, 博士研究生, 主要从事电磁无损检测、裂纹扩展等研究工作。

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出版历程

Defect Classification by Pulsed Eddy Current Technique Based-on Power Spectral Density Analysis

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目录

作者简介:
彭英(1979-), 女, 博士研究生, 主要从事电磁无损检测、裂纹扩展等研究工作。