Eddy Current Imaging Research Articles

Pulse-Modulation Eddy Current Imaging for 3D Profile Reconstruction of Subsurface Corrosion in Metallic Structures of Aviation

Metallic structures of non-ferromagnetic materials such as aluminum alloy, titanium alloy, etc., are widely used in the engineering field of aviation. Owing to harsh and hostile environments, subsurface corrosion manifests itself in the in-service Metallic Structures of Aviation (MSA), and is usually hidden within the structure body, leaving it barely detected. Such defect has been found to be one of the most critical defects posing a severe threat to structural integrity and safety of MSA. Therefore, it is imperative to conduct imaging and quantitative assessment of the subsurface corrosion with effective Non-destructive Evaluation (NDE) techniques. Pulse-modulation Eddy Current technique (PMEC) has been identified to be superior to other electromagnetic NDE techniques in evaluation of corrosion in conductors. In light of this, in this paper PMEC for imaging and 3D profile reconstruction of subsurface corrosion in MSA is investigated. Novel features extracted from testing signals are proposed in a bid to enhance the sensitivity of the PMEC response. An image processing technique based on the modified Theta Map is proposed and utilized for recognition of corrosion opening profile. An efficient inversion method, which is focused on the corrosion-profile reconstruction right within the corrosion region and based on a sectional scanning strategy, is investigated along with the establishment of the database conceiving PMEC responses to regular-shaped corrosion. It has been found from the experimental results that the proposed inversion scheme is feasible and applicable for 3D profiling of subsurface corrosion in MSA with the averaged evaluation accuracy of 95.4%.

Eddy current tomography for visualization of cracks in aircraft riveted joints

The control of the subsurface areas of metal products is necessary in many technological processes. So, for example, in aerospace technology it is essential to determine the presence of defects in aircraft engines. The same problems arise in chemical, power and other industries, which are letting out the highly technological equipment one of the widely known methods for inspecting metal products in the aviation industry is the eddy current method. This method is widely used to control small microscopic defects inside conductive materials. This method allows to ensure the safety of the operation of various products and devices in many areas of modern industry. An eddy current detector (probe) is a device, which induces eddy currents into metal objects and then detects the magnetic fields produced by these eddy currents. A magnetic field is created by a coil, or set of coils, through which a time-varying electrical current is driven. The frequency regime is sufficiently low, a few hertz to a few hundred kilohertz, so the targets of interest are within the near field of the transmitter. Considering the high conductivity of study samples, we can define that used waves in metals are located in the millimeter wave band. An eddy current imaging can be considered near-field imaging and a device allowing obtaining the eddy current images as a scanning near-field microscope. The obtained experimental results showed that the proposed tomographic method is effective for studying various complex inhomogeneities under the metal surface

Near-field linear sampling method for axisymmetric eddy current tomography

This paper is concerned with eddy-current (EC) nondestructive testing of conductive materials and focuses, in particular, on extending the well-known linear sampling method (LSM) to the case of EC equations. We first present the theoretical foundation of the LSM in the present context and in the case of point sources. We then explain how this method can be adapted to a realistic setting of EC probes. In the case of identifying the shape of external deposits from impedance measurements taken from inside of the tube (steam generator), we show how the method can be applied to measurements obtained from a sweeping set of coils. Numerical experiments suggest that good results can be achieved using only a few coils and even in the limiting case of backscattering data.

Inverse Transformation in Eddy Current Tomography with Continuous Optimization of Reference Defect Parameters.

This paper presents a methodology of inverse tomography transformation in eddy current tomography with the use of continuous optimization of reference defect parameters. Ferromagnetic steel samples with rectangular air inclusion defects of known dimensions were prepared and measured using an eddy current tomography setup. FEM-based (Finite Element Method based) forward tomography transformation was developed and utilized in inverse tomography transformation. The presented method of inverse tomography transformation is based on the continuous optimization of parameters that can describe the sample, such as the diameter and dimensions of the reference defect. The obtained results of inverse tomography transformation were in high accordance with the real parameters of the samples. Additionally, the presented method had acceptable repeatability. The obtained values of the sample parameters fit within the range of expanded uncertainty when compared to the real parameters of the sample.

Characterization of image sequences of a defect using pulsed eddy current signals

Pulsed eddy current testing (PECT) has the advantages of rich information, efficiency and noncontact among defect nondestructive testing. Pulsed eddy current imaging technology also has a wide range of applications. However, the imaging method based on single feature is mostly adopted at this stage. In order to make better use of pulsed eddy current signals, signal slice based method was proposed for pulsed eddy current sequence imaging. Through the difference analysis of air-subtracted signal (ASS) and non-defect-subtracted signal (NDSS), sequence imaging in different ways is carried out. The influence of defects was explained by visualizing the diffusion of eddy currents in different specimens using a finite element model. ASS and NDSS image sequences were evaluated through image quality evaluation and quantitative identification. The result shows that there are some image frames in the image sequence that are better than the images generated by a single feature value in the image quality evaluation and quantitative analysis. It proves that the image sequence contains more defect information, which also illustrates the information richness of pulse eddy current detection.

Battery Characterization via Eddy-Current Imaging with Nitrogen-Vacancy Centers in Diamond

Sensitive and accurate diagnostic technologies with magnetic sensors are of great importance for identifying and localizing defects of rechargeable solid batteries using noninvasive detection. We demonstrate a microwave-free alternating current (AC) magnetometry method with negatively charged NV centers in diamond based on a cross-relaxation feature between nitrogen-vacancy (NV) centers and individual substitutional nitrogen (P1) centers occurring at 51.2 mT. We apply the technique to non-destructively image solid-state batteries. By detecting the eddy-current-induced magnetic field of the battery, we distinguish a defect on the external electrode and identify structural anomalies within the battery body. The achieved spatial resolution is μμμ360μm. The maximum magnetic field and phase shift generated by the battery at the modulation frequency of 5 kHz are estimated as 0.04 mT and 0.03 rad respectively.

Real-time defect identification of narrow overlap welds and application based on convolutional neural networks

To improve the quality of narrow overlap welds and reduce cost during the high-strength production, it is essential to detect weld defects promptly by identification of the type of defects to provide solution accordingly. This paper proposes an integrated weld defect identification approach combing eddy current detection with 3D laser scanning based on Convolutional Neural Networks (CNN). The detection principle and equipment of the two detection methods are introduced. To fit the training process of CNN, two set of detection signals are preprocessed: a two-dimensional time-frequency diagram for eddy current signals using continuous wavelet transform and for laser images, weld edges are extracted and divided by region using image convolution and combining with integral graph. CNN model VGG16 is trained afterwards with data collected from one local manufacturer in Shanghai. It is discovered that performance of eddy current and laser image identification on different types of weld defects is different, and the accuracy can be increased with the two methods combined. Last, to achieve real-time detection of narrow overlap welding, a two-stage defect recognition model is built which greatly improves the efficiency of weld defect identification without affecting the accuracy of weld defect identification.

Evaluation of eddy current distortion and field inhomogeneity distortion corrections in MR diffusion imaging using log-demons DIR method

To investigate the feasibility of the log-demons deformable image registration (DIR) method to correct eddy current and field inhomogeneity distortions while preserving diffusion tensor information. Diffusion-weighted images (DWIs) are susceptible to distortions caused by eddy current and echo-planar imaging (EPI) gradients. We propose a post-acquisition correction algorithm using the log-demons DIR technique for eddy current and field inhomogeneity distortions of DWI. The new correction technique was applied to DWI acquired using a diffusion phantom and the multiple acquisitions for standardization of structural imaging validation and evaluation (MASSIVE) brain database. This method is compared to previous methods using cross-correlation, mutual information (MI). In the phantom study, the log-demons algorithm reduced eddy current and field inhomogeneity distortions while preserving diffusion tensor information when compared to affine and demon’s registration techniques. Analysis of the tensor metrics using percent difference and the root mean square of the apparent diffusion coefficient and fractional anisotropy found that the log-demons algorithm outperforms the other algorithms in terms of preserving diffusion information. In the MASSIVE study, the average MI of all slices increased for both eddy current and field inhomogeneity distortion correction. The average absolute differences of all slices between corrected images with opposing gradients were also on average decreased. This work indicates that the log-demons DIR algorithm is feasible to reduce eddy current and field inhomogeneity distortions while preserving quantitative diffusion information.

Pulse-modulation eddy current imaging and evaluation of subsurface corrosion via the improved small sub-domain filtering method

Subsurface corrosion occurring in the conductive structures prone to long-term exposure to harsh and corrosive environments essentially leads to the reduction in the critical area of load carrying members, adversely aggravating the mechanical strength and structural integrity of in-service structures. This has prompted the high demand for non-invasive detection and evaluation of subsurface corrosion using non-destructive evaluation techniques. Pulse-modulation Eddy Current testing (PMEC) has been found to be superior to pulsed eddy current testing in terms of higher sensitivity to subsurface corrosion and accuracy in corrosion imaging. In this paper, a funnel-shaped PMEC probe is utilised for subsurface corrosion imaging along with the inspection sensitivity further enhanced. A novel signal feature of PMEC, namely the averaged peak value is proposed in a bid to improve the signal-to-noise ratio of the PMEC testing, based on which corrosion images are produced. In order to enhance accuracies in image-based profile identification and opening-area assessment of subsurface corrosion, an improve Small Sub-domain Filtering Method (SSFM) together with Canny algorithm is proposed and exploited in the image processing for corrosion-profile recognition. It has been indicated from the experimental results that the subsurface corrosion profile can be well identified using the proposed signal and image processing methods which could keep robust in the testing environment subject to extraneous noises. For the experimental specimens, the accuracy of the subsequent evaluation particularly regarding the corrosion-opening area reaches 94.5% at minimum.

Surface and Subsurface Eddy-Current Imaging With GMR Sensor

The high sensitivity for low-frequency fields makes the tiny giant magnetoresistance (GMR) sensors suitable for high-resolution eddy-current (EC) imaging. In this work, we proposed a novel design of the miniature differential EC probe with a spin-valve GMR sensor for surface and subsurface EC imaging. The differential EC probe consists of a full-bridge GMR chip mounted vertically on the edge of a tiny printed circuit board to reduce the liftoff distance. The excitation signal is perpendicular to the sensing axis of the GMR chip, which makes it sensitive to the unbalance EC signal on the surface of a conductive sample. The miniature EC probe operated at frequencies from 5 to 65 kHz is sensitive to the hidden defects on a flat conductor. The spatial resolution and stability of the device are verified by taking the 2-D scan images over the copper film samples with hidden defects, showing that the spatial resolution is better than 2 mm for the flaw 0.5 mm below the top surface of a multilayer structure. The proposed device is useful in the detection of small surface and subsurface defects, such as cracks, metal loss, and other mechanical damages.

Simulation Study and Application of Eddy Current Array C-scan Imaging Technique to Examine Austenite Stainless Steel

Simulation Study and Application of Eddy Current Array C-scan Imaging Technique to Examine Austenite Stainless Steel

Non-contact strain monitoring of carbon fiber composites using spatial frequency domain eddy current imaging data

A new eddy current testing method for non-contact strain monitoring of continuous carbon fiber-reinforced plastics (CFRPs) was developed. The strain of a CFRP was estimated from a slight change of the fiber orientation measured by eddy current testing. A bidirectional CFRP laminate under off-axis tensile load was scanned by an eddy current probe to obtain the signal change caused by material inhomogeneity. Two-dimensional Fourier transform was applied to the probe signal distribution to identify the fiber orientation of the tested CFRP. The relationship between the fiber orientation and strain was analytically derived using a simple deformation analysis and the classical lamination theory of fiber composites. The strain development predicted by the eddy current method agreed well with that measured by a strain gauge. This method enables non-contact strain monitoring without surface treatment to the tested sample.

Defect Image Recognition and Classification for Eddy Current Testing of Titanium Plate Based on Convolutional Neural Network

In the actual production environment, the eddy current imaging inspection of titanium plate defects is prone to scan shift, scale distortion, and noise interference in varying degrees, which leads to the defect false detection and even missed inspection. In view of this problem, a novel image recognition and classification method based on convolutional neural network (CNN) for eddy current detection of titanium plate defects is proposed. By constructing a variety of experimental conditions and collecting defect signals, the characteristics of eddy current testing (ECT) signals for titanium plate defects are analyzed, and then the convolution structure and learning parameters are set. The structural characteristics of local connectivity and shared weights of CNN have better feature learning and characterization capabilities for titanium plate defect images under scan shift, scale distortion, and strong noise interference. The results prove that, compared with other deep learning and classical machine learning methods, the CNN has a higher recognition and classification accuracy for the defect eddy current image of the titanium plate in the complex detection environment.

A Deep Belief network and Least Squares Support Vector Machine Method for Quantitative Evaluation of Defects in Titanium Sheet Using Eddy Current Scan Image

Titanium (Ti) is an ideal structural material, whose use is gradually emerging in civil engineering. Regular defect evaluation is indispensable during the long-term use of Ti-sheets, which can be performed effectively using eddy current (EC) imaging, a method of visualizing defects that is convenient for inspectors. However, as EC scan images contain abundant information and have discrepancies in terms of their quality, this makes it difficult to extract effective features, thus affecting the evaluation results. In this paper, we propose a method that combines EC imaging technology with a quantitative evaluation method for Ti-sheet defects based on Deep Belief Network (DBN) and Least Squares Support Vector Machine (LSSVM). A multilayer DBN is constructed to extract the effective features from EC scan images for Ti-sheet defects. Based on the extracted feature vectors, a multi-objective regression model of defect dimensions is established using the LSSVM algorithm. Then, the dimensions of Ti-sheet defect such as length, diameter, and depth were quantitatively evaluated by the effective features and the efficient regression model. The experimental results have shown that the evaluation errors for the defect lengths and depths tested were less than 3% and 5% respectively, confirming the validity of the proposed method.

Noninvasive Conductivity and Temperature Sensing Using Magnetic Induction Spectroscopy Imaging

The work presents a perspective in evaluating electromagnetic tomography reconstruction through a spectral eddy current imaging arrangement. Embarking from an established analytical basis, the spectroscopic relation of a metallic conductive body to its physical properties is revealed via multifrequency mutual impedance measurement. Characteristics are evident, from either modeling or experiment, on certain frequency ranges that discriminate the object’s circumstances. Both the amplitude ratio and phase-contrast image spectrum show information on the conductivity and structure of a target considered pivotal for industrial applications. Two test cases are reported: liquid metal structure determination and contactless temperature evaluation of a remote/hidden medium/object. Using eddy current-based spectroscopic imaging data and appropriate calibration, this work, for the first time, demonstrates a novel thermal mapping system. This is a wireless and inductive-based temperature mapping device that can have great potential applications where none of the existing thermal measuring devices could work noninvasively.

Joint use of eddy current imaging and fuzzy similarities to assess the integrity of steel plates

AbstractSteel plates bi-axially loaded are characterized by mechanical deformations whose 2D image representations are very difficult to achieve. In this work, the authors propose an innovative approach based on eddy current techniques for obtaining 2D electrical maps to assess the mechanical integrity of a steel plate. The procedure, also exploiting fuzzy similarity computations, translates the problem of the assessment of the mechanical integrity of a steel plate into a suitable classification problem. The results obtained by this proposed procedure show performances comparable to those provided by well-established soft computing approaches with a higher computational complexity.

Evaluation of debonding in CFRP-epoxy adhesive single-lap joints using eddy current pulse-compression thermography

Contaminations arising during the manufacturing or usage of Carbon Fiber Reinforced Plastic (CFRP)-epoxy adhesive single-lap joints can drastically affect the mechanical performance of the structure and eventually lead to its catastrophic failure. Eddy current stimulated thermography has been recently proposed for detecting and characterizing defects in composite materials. The characterization of debonding in adhesive single-lap joints in composites by eddy current stimulated thermography can be challenging due to the anisotropic thermal diffusion caused by the anisotropic conductivity and multi-layer nature of the composites. This work tackles the challenge of detecting the interface contamination by combining eddy current pulse-compression thermography and image post-processing algorithms. The impulse responses from samples containing either brass, release film or release agent contaminations obtained through pulse-compression combined with Eddy Current Pulsed Thermography (ECPT). Non-uniform heating patterns are removed by partial least square technique. Then, the time instants containing meaningful information about the contaminated interface layer are inferred by comparing each norm of kernel principal component over suitable time windows. The evaluation of contamination depths and properties are conducted in selected time windows by principal component analysis and time-phase analysis. Results proved that release agent contamination plays the minor role in changing the electrical and thermal properties of the single-lap joints compared with brass and release film.

Comparison of time and frequency domain features’ immunity against lift-off in pulse-compression eddy current imaging

This paper reports a comparison between imaging procedures based on time- and frequency-domain features applied to eddy current data collected on an Aluminium alloy benchmark sample. Both the defect detection capability and the immunity against lift-off are analysed. Historically, the pulsed eddy current method was introduced to improve the performance of eddy current testing both in terms of increased penetration depth and lift-off invariance by exploiting time-domain features analysis, commonly precluded when using single- or multi-tone excitations. In this work, such analysis is instead accomplished by using a swept-frequency excitation signal along with an optimized pulse-compression procedure. This approach allows the user to easily tune the energy delivered to the system by increasing the sweep duration to improve on demand the resultant Signal-to-Noise Ratio (SNR) without losing the capability of time-domain feature extraction. The imaging procedure makes use of amplitude and phase features of both frequency- and time-domain data where time-phase is defined through the Hilbert transform of the pulse-compression output. The detection capability of various imaging strategies, namely A-, B- and C-scans, are compared in terms of inspection depth and lift-off robustness by using the SNR merit factor. It is shown that time-domain features outperform frequency-based ones in terms of SNR for the case of deeper defects and that phase features are robust against lift-off variations for both time and frequency domains. In addition, the analysis of time-amplitude images clearly shows the presence of lift-off invariant points. To our knowledge, this is the first experimental evidence of the lift-off invariance points retrieved after applying pulse-compression in combination with coded excitation instead of using directly pulsed, multi-tone or single-tone sinusoidal signals. This not only confirms previous results achieved by the authors but also demonstrates that pulse-compression eddy current can represent a solution to combine the advantages of pulsed and sinusoidal excitation strategies.

Eddy current O-scan and C-scan imaging techniques for macrocrack detection in silicon solar cells

ABSTRACTCrack detection is important in the quality control of silicon-based solar cells (SSC). Silicon wafers are semi-conductive and cracks disturb the local conductivity and even induce anisotropy in conductivity of SSC. Eddy current method, which can detect the change of conductivity in the material, is a potential tool for detecting cracks in SSC. In this study, an experimental set-up is proposed to conduct both eddy current O-scan and C-scan imaging to the SSC. The eddy current O-scan was firstly used to investigate the anisotropy in the conductivity of SSC. Through comparing the inherent and crack-induced anisotropy in impedance (amplitude ratio and phase lag), macrocracks could be detected. The changes in the measured directional impedance could roughly reflect the shape of the cross-type macrocrack. In the C-scan mode, multiple slit defects were prepared to simulate macrocracks. Though the lateral resolution of the sensor was limited, the crack detection ability of the sensor could be evaluated with the C-scan imaging results. A slit defect with a width of 0.5 mm and a length of 1 mm was detected. A new criteria, the distance between feature zero-crossing points in the first derivative of the phase lag curve accurately reflected the actual length of the slit.

Method for enhancement of spatial resolution of eddy current imaging

We propose a method for spatial resolution enhancement of eddy current images obtained by magnetic induction 2D scanning. A measurement technique based on a scanning differential source of an alternating magnetic field combined with a receiver is considered. A flat spiral coil is used as a receiver. The dimensions of the receiver and source coils are larger than or comparable with the dimensions of the objects under study. We represent an electrically conductive object as a set of small diameter conductive coils with a specific current induced by an external source. The method proposed solves the forward and inverse problems for linear coefficients describing the distribution of small coils representing a flat conductive object of arbitrary shape. This method was verified numerically and experimentally. The visualization of the object at a range of 15 mm with appropriate spatial resolution is shown experimentally at a frequency of 30 kHz. This method could be applied for the non-destructive testing and visualization of metallic objects.The novelty of the proposed method lies in the fact that it provides the ability to use probe coils with sizes much larger than inhomogeneities. We also proposed a method for calculating the hardware function of the system and solving the inverse problem. Detailed object visualization with sizes smaller than the probe system of the coils is provided. The possibility of increasing the resolution by seven times at a noise level of 0.1% is shown numerically.