[1]李阳,朱文博,静丰羽,等.基于局部离群因子与隔离森林的激光超声超声缺陷检测[J].郑州大学学报(工学版),2024,45(pre):2.[doi:10. 13705 / j. issn. 1671-6833. 2025. 01. 003]
　LI Yang,ZHU Wen Bo,JING Fengyu,et al.Laser Ultrasonic Defect Detection Based on Loca; Outlier Factor and Isolated Forest[J].Journal of Zhengzhou University (Engineering Science),2024,45(pre):2.[doi:10. 13705 / j. issn. 1671-6833. 2025. 01. 003]
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基于局部离群因子与隔离森林的激光超声超声缺陷检测()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
45
期数:
2024年pre
页码:
2
栏目:
出版日期:
2024-12-31

文章信息/Info

Title:
Laser Ultrasonic Defect Detection Based on Loca; Outlier Factor and Isolated Forest
作者:
李阳1; 朱文博1; 静丰羽2; 叶中飞3; 马云瑞3; 周洋1; 邹云1
(1.郑州大学机械与动力工程学院,河南 郑州 450001 ; 2.中国电建集团河南电力器材有限公司,河南 漯河4620003.国网河南省电力公司电力科学研究院,河南 郑州 450052 )
Author(s):
LI Yang1;  ZHU Wen Bo1;  JING Fengyu 2;  YE Zhong Fei3;  MA Yun Rui3;  ZHOU Yang 1;  ZOU Yun 1
School of Zhengzhou University and Power Engineering, Zhengzhou 450001, China 2. POWERCHINA Henan Electric Power Equipment Co., Ltd. Luohe 462000, China; 3. State Grid Henan Electric Power Research Institute, Zhengzhou, 450052, China
关键词:
激光超声; 缺陷检测; 主成分分析; 局部离群因子; 隔离森林; 铝合金
Keywords:
laser ultrasonic;  defect detection;  principal component analysis;  isolated forest;  local outlier factor;  aluminium alloy
分类号:
TH16 ；TB551
DOI:
10. 13705 / j. issn. 1671-6833. 2025. 01. 003
文献标志码:
A
摘要:
针对激光超声缺陷检测中最大振幅图存在伪像的问题，本研究结合了主成分分析（PCA）和两种无监督的机器学习算法：局部离群因子（LOF）和隔离森林（IF），以实现对LU数据的无监督异常检测。首先，利用PCA算法对LU数据进行降维处理，减轻了LU数据的复杂度；然后，利用LOF和IF算法进行了数据异常值的识别分析，并利用累积分布函数和核密度估计确定异常值的阈值大小；最后，对比了LOF、IF算法以及最大振幅图的检测结果后，发现：LOF算法有更优的缺陷识别精度和更低的误判率。
Abstract:
In response to the issue of artifacts in the maximum amplitude images in laser ultrasonic defect detection, this study integrates Principal Component Analysis (PCA) with two unsupervised machine learning algorithms: Local Outlier Factor (LOF) and Isolation Forest (IF), to perform unsupervised anomaly detection on LU data. Initially, the PCA algorithm is used to reduce the dimensionality of the LU data, simplifying its complexity. Subsequently, the LOF and IF algorithms are employed to identify outliers in the data, and the thresholds for these outliers are determined using the cumulative distribution function and kernel density estimation. Finally, a comparison of the detection results from the LOF, IF algorithms, and the maximum amplitude images reveals that the LOF algorithm offers superior defect detection precision and a lower false positive rate

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更新日期/Last Update: 2024-10-10