Rapid Identification of Material Defects Based on Pulsed Multifrequency Eddy Current Testing and the k-Nearest Neighbor Method

被引：6

作者：

Grochowalski, Jacek M. ^{[1
]}

Chady, Tomasz ^{[1
]}

机构：

[1] West Pomeranian Univ Technol Szczecin, Fac Elect Engn, PL-70313 Szczecin, Poland

来源：

MATERIALS | 2023年 / 16卷 / 20期

关键词：

multifrequency excitation and spectrogram eddy current testing; nondestructive testing; k-Nearest Neighbors (k-NN) algorithm; eddy currents; finite element analysis; numerical simulations; INSPECTION;

D O I：

10.3390/ma16206650

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070304 ; 081704 ;

摘要：

The article discusses the utilization of Pulsed Multifrequency Excitation and Spectrogram Eddy Current Testing (PMFES-ECT) in conjunction with the supervised learning method for the purpose of estimating defect parameters in conductive materials. To obtain estimates for these parameters, a three-dimensional finite element method model was developed for the sensor and specimen containing defects. The outcomes obtained from the simulation were employed as training data for the k-Nearest Neighbors (k-NN) algorithm. Subsequently, the k-NN algorithm was employed to determine the defect parameters by leveraging the available measurement outcomes. The evaluation of classification accuracy for different combinations of predictors derived from measured data is also presented in this study.

引用

页数：23

共 50 条

[41] Hybrid support vector machine and K-nearest neighbor-based software testing for educational assistant
Ramesh, Lilly
Radhika, S.
Jothi, S.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2023, 35 (01):
[42] A novel nonlinear QSAR method based on K-nearest neighbor principle and variable selection.
Zheng, WF
Tropsha, A
ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 1998, 215 : U512 - U512
[43] Heterogeneous graph community detection method based on K-nearest neighbor graph neural network
Liu, Xiaoyang
Wu, Yudie
Fiumara, Giacomo
De Meo, Pasquale
INTELLIGENT DATA ANALYSIS, 2024, 28 (06) : 1445 - 1466
[44] K-Nearest Neighbor Model based Short-Term Traffic Flow Prediction Method
Yang, Lijin
Yang, Qing
Li, Yonghua
Feng, Yuqing
2019 18TH INTERNATIONAL SYMPOSIUM ON DISTRIBUTED COMPUTING AND APPLICATIONS FOR BUSINESS ENGINEERING AND SCIENCE (DCABES 2019), 2019, : 27 - 30
[45] Real-time Highway Traffic Accident Prediction Based on the k-Nearest Neighbor Method
Lv, Yisheng
Tang, Shuming
Zhao, Hongxia
2009 INTERNATIONAL CONFERENCE ON MEASURING TECHNOLOGY AND MECHATRONICS AUTOMATION, VOL III, 2009, : 547 - +
[46] A Fault Detection Method Based on Weighted Dynamic K-nearest Neighbor and Diffusion Mapping for Process
Li, Cheng
Xu, Jinxue
Li, Yuan
2018 37TH CHINESE CONTROL CONFERENCE (CCC), 2018, : 5991 - 5996
[47] A Fault Detection Method Based on the Dynamic k-Nearest Neighbor Model and Dual Control Chart
Liu, Liang
Liu, Jianchang
Wang, Honghai
Tan, Shubin
Liu, Yuanchao
Yu, Miao
Xu, Peng
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2024, 21 (04) : 6819 - 6832
[48] Classification based on K-Nearest Neighbor and Logistic Regression method of coffee using Electronic Nose
Prehanto, D. R.
Indriyanti, A. D.
Nuryana, I. K. D.
Permadi, G. S.
5TH ANNUAL APPLIED SCIENCE AND ENGINEERING CONFERENCE (AASEC 2020), 2021, 1098
[49] A new K-nearest neighbor classification method based on belief functions in wireless sensor networks
Zhang, Yang
Yuan, Deyang
Journal of Computers (Taiwan), 2021, 32 (03): : 263 - 273
[50] Prediction of carbamylated lysine sites based on the one-class k-nearest neighbor method
Huang, Guohua
Zhou, You
Zhang, Yuchao
Li, Bi-Qing
Zhang, Ning
Cai, Yu-Dong
MOLECULAR BIOSYSTEMS, 2013, 9 (11) : 2729 - 2740

← 1 2 3 4 5 →