scholarly journals New Proposal for Inverse Algorithm Enhancing Noise Robust Eddy-Current Non-Destructive Evaluation

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5548
Author(s):  
Milan Smetana ◽  
Lukas Behun ◽  
Daniela Gombarska ◽  
Ladislav Janousek
Keyword(s):  
Eddy Current ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
Noise Robustness ◽  
Current Response ◽  
Inverse Algorithm ◽  
Material Defects ◽  
Non Destructive Evaluation ◽  
Noise Robust ◽  
Non Destructive

Solution of inverse problem in eddy-current non-destructive evaluation of material defects is concerned in this study. A new inverse algorithm incorporating three methods is proposed. The wavelet transform of sensed eddy-current responses complemented by the principal component analysis and followed by the neural network classification are employed for this purpose. The goal is to increase the noise robustness of the evaluation. The proposed inverse algorithm is tested using real eddy-current response data gained from artificial electro-discharge machined notches made in austenitic stainless-steel biomaterial. Eddy-current responses due to the material defects are acquired using a newly developed eddy-current probe that senses separately three spatial components of the perturbed electromagnetic field. The presented results clearly show that the error in evaluation of material defect depth using the proposed algorithm is less than 10% even when the signal-to-noise ratio is as high as 10 dB.

Novel insight into swept frequency eddy-current non-destructive evaluation of material defects

Measurement ◽  
2018 ◽  
Vol 116 ◽  
pp. 246-250 ◽  
Author(s):  
Ladislav Janousek ◽  
Andrea Stubendekova ◽  
Milan Smetana
Keyword(s):  
Eddy Current ◽  
Material Defects ◽  
Non Destructive Evaluation ◽  
Non Destructive ◽  
Insight Into

Review on Non-Destructive Testing Technique of Eddy Current Pulsed Thermography

2015 ◽  
Vol 742 ◽  
pp. 128-131 ◽  
Author(s):  
Jian Min Zhou ◽  
Jun Yang ◽  
Qi Wan
Keyword(s):  
Eddy Current ◽  
Signal Reconstruction ◽  
Infrared Image ◽  
Principal Component ◽  
Research Progress ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Pulsed Thermography ◽  
Pulsed Phase Thermography ◽  
Non Destructive

This paper introduces the theory of eddy current pulsed thermography and expounds the research status of eddy current pulsed thermography in application and information extraction. Thermographic signal reconstruction, pulsed phase thermography, principal component analysis were introuduced in this paper and listed some fusion multiple methods to acquire information from infrared image. At last, it summarizes research progress, existing problem and deelopment of eddy current pulsed thermography.

Eddy current technique with high temperature SQUID for non-destructive evaluation of non-magnetic metallic structures

Cryogenics ◽  
1996 ◽  
Vol 36 (2) ◽  
pp. 83-86 ◽  
Author(s):  
Y. Tavrin ◽  
H.-J. Krause ◽  
W. Wolf ◽  
V. Glyantsev ◽  
J. Schubert ◽  
...  
Keyword(s):  
High Temperature ◽  
Eddy Current ◽  
Metallic Structures ◽  
Non Destructive Evaluation ◽  
Current Technique ◽  
Non Destructive

Optimum eddy current excitation frequency for subsurface defect detection in SQUID based non-destructive evaluation

2010 ◽  
Vol 43 (8) ◽  
pp. 713-717 ◽  
Author(s):  
R. Nagendran ◽  
N. Thirumurugan ◽  
N. Chinnasamy ◽  
M.P. Janawadkar ◽  
R. Baskaran ◽  
...  
Keyword(s):  
Defect Detection ◽  
Eddy Current ◽  
Excitation Frequency ◽  
Non Destructive Evaluation ◽  
Subsurface Defect ◽  
Non Destructive

scholarly journals Automated Health Condition Diagnosis of in situ Wood Utility Poles Using an Intelligent Non-Destructive Evaluation (NDE) Framework

2020 ◽  
Vol 20 (10) ◽  
pp. 2042002 ◽  
Author(s):  
Yang Yu ◽  
Mahbube Subhani ◽  
Azadeh Noori Hoshyar ◽  
Jianchun Li ◽  
Huan Li
Keyword(s):  
Communication Networks ◽  
Guided Waves ◽  
Power Transmission ◽  
Influence Factors ◽  
Principal Component ◽  
Field Testing ◽  
Health Condition ◽  
Non Destructive Evaluation ◽  
Utility Poles ◽  
Non Destructive

Wood utility poles are widely applied in power transmission and telecommunication systems in Australia. Because of a variety of external influence factors, such as fungi, termite and environmental conditions, failure of poles due to the wood degradation with time is of common occurrence with high degree uncertainty. The pole failure may result in serious consequences including both economic and public safety. Therefore, accurately and timely identifying the health condition of the utility poles is of great significance for economic and safe operation of electricity and communication networks. In this paper, a novel non-destructive evaluation (NDE) framework with advanced signal processing and artificial intelligence (AI) techniques is developed to diagnose the condition of utility pole in field. To begin with, the guided waves (GWs) generated within the pole is measured using multi-sensing technique, avoiding difficult interpretation of various wave modes which cannot be detected by only one sensor. Then, empirical mode decomposition (EMD) and principal component analysis (PCA) are employed to extract and select damage-sensitive features from the captured GW signals. Additionally, the up-to-date machine learning (ML) techniques are adopted to diagnose the health condition of the pole based on selected signal patterns. Eventually, the performance of the developed NDE framework is evaluated using the field testing data from 15 new and 24 decommissioned utility poles at the pole yard in Sydney.

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