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Electrical approach to defect depth estimation by stepped infrared thermography

IEE PROCEEDINGS-SCIENCE MEASUREMENT AND TECHNOLOGY 2004年第4期151卷 298-304页

作者： Gupta, R Tuli, S Indian Inst Technol Ctr Appl Res Elect New Delhi 110016 India

An electrical modelling and analysis is presented for thermal nondestructive evaluation of materials by stepped infrared thermography. A one-dimensional electrical analysis based on the Laplace transform technique of network-analysis and time delay in an RC ladder network is given for defect depth estimation. defect depth is evaluated based on the time instants at which surface temperature evolution over the defect and nondefect regions of the material deviates from its constant initial slope, corresponding to the response of a semi-infinite material under similar conditions of step heating. The method is applicable even for three-dimensional geometries of materials having unknown thermal properties and variable surface emissivity. Experimental and simulated results validate the proposed method and give good estimation of defect depth, even under noisy conditions for a thermally anisotropic material and a nonflat bottom type of defect.

关键词： three-dimensional geometry thermally anisotropic material Materials testing ladder networks Nondestructive testing: optical methods carbon fibre reinforced plastics one-dimensional electrical analysis thermal properties time delay surface emissivity defect depth estimation electrical model semiinfinite material delay circuits infrared thermography C Thermal instruments and techniques nondefect regions network-analysis carbon fibres nondestructive testing Filters and other networks Laplace transform technique Laplace transforms Composite materials (engineering materials science) noisy conditions thermal nondestructive evaluation Detection of radiation (bolometers, photoelectric cells, i.r. and submillimetre waves detection) surface temperature evolution infrared imaging RC ladder network carbon steel

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Analysis of the accuracy of a neural algorithm for defect depth estimation using PCA processing from active thermography data

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INFRARED PHYSICS & TECHNOLOGY 2013年 56卷 1-7页

作者： Dudzik, S. Czestochowa Tech Univ Dept Elect Engn PL-42200 Czestochowa Poland

In the paper a neural algorithm, which uses an active thermography for defect depth estimation, is presented. Simulations of the algorithm, for three datasets representing different phases of the heat transfer process developing in the test sample were performed. The influence of the emissivity error of the test sample surface on the accuracy of defect depth estimation is discussed. The investigations were performed for test sample made of the material with low thermal diffusivity. (c) 2012 Elsevier B.V. All rights reserved.

关键词： Active thermography Non-destructive evaluation Neural networks defect depth estimation

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Multi-frequency ECT Method for defect depth estimation

Multi-frequency ECT Method for Defect Depth Estimation

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IEEE Sensors Applications Symposium (SAS)

作者： Bernieri, Andrea Betta, Giovanni Ferrigno, Luigi Laracca, Marco Univ Cassino Dept Automat Electromagnetism Informat Engn & Ind Cassino FR Italy

ISBN: (纸本)9781457717239

Scientific research on non-destructive testing is constantly looking for new solutions, about sensors and digital signal processing techniques, to improve the detection and characterization of defects in materials. The authors have engaged this particular line of research with particular reference to non-destructive testing applied to the conductive material through the use of eddy currents. After the realization of a novel instrument based on a Giant Magneto-Resistance probe, in this paper they propose the use of a suitable multi-frequency approach to improve reliability and accuracy in the defect estimation. Key features of the proposed instrument are the capability to detect, locate, and characterize thin defects as superficial and sub-superficial cracks. A number of tests carried out both in simulation and real environments show the goodness of the proposal.

关键词： component multifrequency eddy current testing GMR sensor defect depth estimation

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A two-stage pipeline defect depth estimation method based on MFL signals 34

A two-stage pipeline defect depth estimation method based on...

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34th Chinese Control and Decision Conference (CCDC)

作者： Wang, Lei Liu, Jinhai Qu, Fuming Zuo, Fengyuan Jiang, Lin Shen, XiangKai Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Peoples R China Univ Sci & Technol Beijing Sch Civil & Resource Engn Beijing 100083 Peoples R China

ISBN: (纸本)9781665478960

Magnetic flux leakage (MFL) internal inspection is widely used in pipeline non-destructive testing (NDT) with its low cost and high efficiency. Affected by different environments, MFL signals are complex and changeable, which has brought severe challenges to the accurate estimation of defect depth. In this paper, a novel pipeline defect depth estimation method is proposed to improve the accuracy, which can extract deep features from MFL signals adaptively. First, an auto-encoder is designed to extract primary features from MFL signals in an unsupervised way. Second, a classification neural network is developed to extract secondary features in a supervised way. To make better use of the feature information, part of the weight and structure from auto-encoder is reused in this network. Third, a random forest model is constructed to study the complex nonlinear relationship between features and defect depth. Experimental results are compared with state-of-art method from seven aspects, which highlight the superiority of the proposed method in defect depth estimation.

关键词： defect depth estimation deep features MFL signals pipeline defect

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A two-stage pipeline defect depth estimation method based on MFL signals

A two-stage pipeline defect depth estimation method based on...

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第34届中国控制与决策会议

作者： Lei Wang Jinhai Liu Fuming Qu Fengyuan Zuo Lin Jiang XiangKai Shen School of Information Science and Engineering Northeastern University School of Civil and Resource Engineering University of Science and Technology Beijing

ISBN: (纸本)9781665478977

Magnetic flux leakage(MFL) internal inspection is widely used in pipeline non-destructive testing(NDT)with its low cost and high *** by different environments,MFL signals are complex and changeable,which has brought severe challenges to the accurate estimation of defect *** this paper,a novel pipeline defect depth estimation method is proposed to improve the accuracy,which can extract deep features from MFL signals ***,an auto-encoder is designed to extract primary features from MFL signals in an unsupervised ***,a classification neural network is developed to extract secondary features in a supervised *** make better use of the feature information,part of the weight and structure from auto-encoder is reused in this ***,a random forest model is constructed to study the complex nonlinear relationship between features and defect *** results are compared with state-of-art method from seven aspects,which highlight the superiority of the proposed method in defect depth estimation.

关键词： defect depth estimation deep features MFL signals pipeline defect

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depth estimation of internal defects from a sensitive frequency using thermal wave radar

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NDT & E INTERNATIONAL 2025年 154卷

作者： Zhuo, Lijun Xu, Yifan Zhu, Jianguo Chen, Hongchu Yang, Qiang Jiangsu Univ Fac Civil Engn & Mech Zhenjiang 212013 Peoples R China Jiangsu Univ Inst Struct Hlth Management Zhenjiang 212013 Peoples R China Zhejiang Univ Sch Aeronaut & Astronaut Hangzhou 310027 Peoples R China Harbin Inst Technol Natl Key Lab Sci & Technol Adv Composites Special Harbin 150080 Peoples R China

Thermal wave radar is one of the active infrared thermographic techniques that detects defects by applying a broadband frequency modulated excitation and cross-correlation. Although it outperforms conventional methods in the depth resolvability and defect detectability, there still exist difficulties in the estimation of defect depth due to the distortion effect of heat diffusion and noises. This work aims to estimate the depth of an internal defect using a new parameter instead of the widely used blind frequency, namely the sensitive frequency, at which a maximum phase contrast is reached. The thermal wave radar is implemented by using a linear frequency modulated laser excitation. The phase contrast in function of frequency is obtained from the dual orthogonal demodulation algorithm. A linear relationship with calibrated coefficients is established between the sensitive frequency and the reciprocal of the square of depth. The effect of defect size on the linear relationship is studied, and a correction of the linear relationship is proposed to improve the accuracy of depth estimation. The proposed method is numerically verified and experimentally validated, and the results illustrated that the depths of defects with various aspect ratios can be well estimated.

关键词： Non-destructive evaluation Thermal wave radar defect depth estimation Phase contrast Calibration

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A Simplified Adaptive Fuzzy Min-Max Neural Network for pattern classification

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NEUROCOMPUTING 2025年 630卷

作者： Fu, Mingrui Chen, Shuai Wei, Xiaoxiao Du, Jinsong Wang, Wei Liu, Jinhai Chinese Acad Sci Shenyang Inst Automat Shenyang 110016 Peoples R China Key Lab Intelligent Detect & Equipment Technol Lia Shenyang 110169 Peoples R China Liaoning Liaohe Lab Shenyang 100169 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

neural network, as a prominent hyperbox classifier, has undergone significant research over the past three decades. Despite its effectiveness, the conventional FMM algorithm relies on a fixed expansion coefficient, which often leads to suboptimal performance because it cannot adapt to varying data distributions. Additionally, the membership function in FMM typically depends on sensitive parameters that need to be preset, further complicating its practical application. To address these limitations and improve the adaptability and classification accuracy of FMM, we propose a Simplified Adaptive Fuzzy Min-Max Neural Network (SAFMM) for pattern classification, where all parameters are adaptively chosen. Our contributions are twofold: Firstly, a novel FMM learning algorithm is introduced, where hyperboxes expand adaptively instead of using a fixed coefficient, and cluster and class-specific hyperboxes are simultaneously applied to avoid overlap. Secondly, considering uneven data distributions within hyperboxes, a new adaptive membership function based on data core is defined for classification. The proposed SAFMM model is evaluated on benchmark datasets and areal- world case of magnetic flux leakage (MFL) defect depth estimation in pipeline non-destructive testing (NDT). Experimental results demonstrate that SAFMM can generate fewer hyperboxes while ensuring classification accuracy and exhibits an absolute advantage for quick classification as the dataset size increases.

关键词： Fuzzy min-max neural network Pattern classification MFL defect depth estimation

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Determination of defect depth and Size Using Virtual Heat Sources in Pulsed Infrared Thermography

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EXPERIMENTAL MECHANICS 2013年第4期53卷 661-671页

作者： Manohar, A. di Scalea, F. Lanza Univ Calif San Diego Dept Struct Engn NDE & SHM Lab La Jolla CA 92093 USA

The determination of defect depth and size using Pulsed Infrared Thermography is a critical problem. The problem of defect depth estimation has been previously studied using 1D heat conduction models. Unfortunately, 1D heat conduction based models are generally inadequate in predicting heat flow around defects. In this study, a novel approach based on virtual heat sources is proposed to model heat flow around defects accounting for 2D axisymmetric heat conduction. The proposed approach is used to quantitatively determine the defect depth and size. The validity of the model is established using experiments performed on a stainless steel plate specimen with flat bottom holes at different depths.

关键词： Infrared Thermography Pulsed Thermography Nondestructive Testing defect depth estimation Virtual heat sources

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Crack depth estimation by Using a Multi-Frequency ECT Method

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2013年第3期62卷 544-552页

作者： Bernieri, Andrea Betta, Giovanni Ferrigno, Luigi Laracca, Marco Maurizio Scarano Univ Cassino Dept Elect & Informat Engn I-03043 Cassino FR Italy

In many industrial application fields as manufacturing, quality control, and so on, it is very important to highlight, to locate, and to characterize the presence of thin defects (cracks) in conductive materials. The characterization phase tries to determine the geometrical characteristics of the thin defect namely the length, the width, the height, and the depth. The analysis of these characteristics allows the user in accepting or discarding realized components and in tuning and improving the production chain. The authors have engaged this line of research with particular reference to non-destructive testing applied to the conductive material through the use of eddy currents. They realized methods and instruments able to detect, locate, and characterize thin defects. In this paper, a novel measurement method able to improve the characterization of the crack depth is proposed. It is based on the use of a suitable multi-frequency excitation signals and of digital signal processing algorithms. Tests carried out in an emulation environment have shown the applicability of the method and have allowed the tuning of the measurement algorithm. Tests carried out in a real environment confirm the goodness of the proposal.

关键词： defect depth estimation GMR sensor multi-frequency eddy current testing

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A novel biaxial probe implementing multifrequency excitation and SVM processing for NDT

A novel biaxial probe implementing multifrequency excitation...

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IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

作者： Bernieri, Andrea Betta, Giovanni Ferrigno, Luigi Laracca, Marco Univ Cassino Dept Elect & Informat Engn Maurizio Scarano Cassino FR Italy

ISBN: (纸本)9781467346214

The paper proposes a hand held eddy current based measurement instrument for the detection of thin defect on conductive planar specimens. The measurement instrument adopts a Giant Magneto Resistance based probe to detect the eddy current reaction field from the specimen and a suitable positioning system to measure the sensor position in the specimen plane. Two main novelties are proposed respect to previous realizations by the same authors: a) the improvement of the probe through the use of a bi-axial magnetic field sensor and the implementation of a suitable multi-frequency excitation strategy;b) the realization of a new processing section, based on an optimized SVM regressor, for the reliable estimation of the position and the geometrical characteristics of the thin defect. Results obtained on both simulated environments and real specimens showed the goodness of the proposal and the ability in the geometrical characterization of the thin defect.

关键词： multifrequency eddy current testing GMR sensor defect depth estimation nondestructive testing signal processing support vector machine

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