Eddy Current Nondestructive Testing Research Articles

An improved PSO-SVM model for online recognition defects in eddy current testing

Accurate and rapid recognition of defects is essential for structural integrity and health monitoring of in-service device using eddy current (EC) non-destructive testing. This paper introduces a novel model-free method that includes three main modules: a signal pre-processing module, a classifier module and an optimisation module. In the signal pre-processing module, a kind of two-stage differential structure is proposed to suppress the lift-off fluctuation that could contaminate the EC signal. In the classifier module, multi-class support vector machine (SVM) based on one-against-one strategy is utilised for its good accuracy. In the optimisation module, the optimal parameters of classifier are obtained by an improved particle swarm optimisation (IPSO) algorithm. The proposed IPSO technique can improve convergence performance of the primary PSO through the following strategies: nonlinear processing of inertia weight, introductions of the black hole and simulated annealing model with extremum disturbance. The good generalisation ability of the IPSO-SVM model has been validated through adding additional specimen into the testing set. Experiments show that the proposed algorithm can achieve higher recognition accuracy and efficiency than other well-known classifiers and the superiorities are more obvious with less training set, which contributes to online application.

Computational transformation of signals of a measuring information system for eddy-current flaw detection

The features of a computational-transformation algorithm that is used for engineering implementation of an eddy-current nondestructive testing method using a combined multisector transducer with a magnetic field at different frequencies are described.

Eddy-current non-destructive testing system for the determination of crack orientation

Detecting the orientation of deep oriented cracks is a major challenge in the development of Eddy-Current (EC) Non-Destructive Testing (NDT). In fact, the detection sensitivity of EC-NDT depends on the interaction between the crack length direction and the EC flowing in the materials. In conventional EC-NDT systems, the induced currents are primarily generated along a single direction in the tested sample. This paper presents a new excitation method for generating a pseudo-rotating ac magnetic field and, consequently, pseudo-rotating eddy currents. This method significantly improves the detection of deep cracks of any orientation. The detected signal due to the crack is measured by an Improved Giant Magneto-Resistance Magnetometer (IGMRM). The magnetic flux density signature of the crack is studied using a 3D finite element model. Numerical and experimental results demonstrate the potential and the efficiency of this method.

Novel Coupled Electric Field Method for Defect Characterization in Eddy Current Non-destructive Testing Systems

This article presents a defect modeling in eddy current non-destructive testing systems by using a new developed method called coupled electric field. It permits to improve qualitatively several models developed so far by many authors using coupled circuit methods that consider the defect only as loss of material. However, a defect can occur with a finite conductivity such as impurity, small burns and micro-solder. For this reason, this investigation consists of extending the coupled circuit method to the modeling of this kind of defects. The proposed approach consists of firstly considering the defect as an electric conductive volume and secondly changing the state variable presenting the electric current by the electric field one. This procedure permits expressing explicitly the impedance variation caused by the presence of an axi-symmetrical defect according to its characteristics. The comparison between the impedance variations calculated using finite elements method and the proposed one demonstrates a very good concordance. After this validation, the study covers also the influence of the defect shape and position on encircling probe impedance. This method is interesting since it permits a fully characterization of this kind of defects and facilitates the inversion process. Moreover, using a 3D finite element observation, this fast tool of simulation can be adapted for a fast phenomenological modeling of asymmetrical configurations.

Eddy Current Nondestructive Testing for Carbon Fiber- Reinforced Composites

This paper describes the use of an eddy current testing (ECT) method for the inspection and detection of impact damage in carbon fiber-reinforced composites (CFRP). ECT method is a nondestructive testing (NDT) method where electric induction is used. This method is widely used for detecting cracks and corrosion in metals, or checking their electric conductivity. Because carbon fiber in CFRP has electric conductivity, ECT method has a potential to inspect the defects. However, electric conductivity of CFRP is much smaller than that of metals. Moreover, from the view of the eddy current probe, CFRP to be checked looks as a inhomogeneous conductive materials where conductive fibers are bundled and laid up, and this is completely different situation comparing with metal samples that are homogeneous. Therefore, there are several problems to be solved for applying ECT method to CFRP such as proper selection of test frequency, shape of probe, or signal processing.

Dimension Estimation of Rectangular Cracks Using Impedance Changes of the Eddy Current Probe with a Neural Network

In this paper a method for estimating the dimension of rectangular cracks is proposed. The use of Eddy current (EC)nondestructive testing (NDT) based on probe impedance changes on the crack regions is considered. The artificialneural network estimates the dimension of new cracks using impedance changes of the eddy current probe. Theexperimental results and finite element method (FEM) results are used for training the artificial neural network. Byincreasing the number of experiments, the results of the finite element method are not necessary. The simulationresults are very promising.

Fast Analytical Modeling of Eddy Current Non-Destructive Testing of Magnetic Material

This article presents the modeling of non-destructive testing systems containing magnetic materials using a fast numerical method. Its main aim consists of correcting the half analytical expression of the impedance variation, formulated by some authors, caused by the presence of a conducting plate below of an absolute ferrite core probe. The obtained results of this correction are found to be consistent and satisfactory comparatively to those of finite element method. It also deals with the study the method rapidity by comparing its simulation time to that of the finite element method. As result, the proposed method is found to be very fast and a very short simulation time is required to calculate the sensor impedance. Indeed, for the studied system the coupled circuit simulation time is lower than 1.09 s. This study is appreciable, since it permits to solve quickly the inverse problem by expressing the physical and geometrical features of the material or defect according to the measured parameters. More importantly, this method is applicable to any axi-symmetric systems and can be adapted for the simulation of three-dimensional configurations.

Defect Characterization With Eddy Current Testing Using Nonlinear-Regression Feature Extraction and Artificial Neural Networks

The estimation of the parameters of defects from eddy current nondestructive testing data is an important tool to evaluate the structural integrity of critical metallic parts. In recent years, several works have reported the use of artificial neural networks (ANNs) to deal with the complex relation between the testing data and the defect properties. To extract relevant features used by the ANN, principal component analysis, wavelet decomposition, and the discrete Fourier transform have been proposed. In this paper, a method to estimate dimensional parameters from eddy current testing data is reported. Feature extraction is based on the modeling of the testing data by a template of additive Gaussian functions and nonlinear regressions to estimate their parameters. An ANN was trained using features extracted from a synthetic data set obtained with finite-element modeling of the eddy current probe. The proposed method was applied to both simulated and measured data, providing good estimates.

A Novel Technique for Eliminating Probe Lift-Off in Eddy Current Nondestructive Testing

The lift-off problem is a very important problem in eddy current testing, which will influence the measurement accuracy. This paper proposes a novel technique for eliminating the probe lift-off in eddy current nondestructive testing. Firstly, the basic principles and characteristics of eddy current testing were introduced. Secondly, this paper analyzed and studied the coil impedance responses caused by the variations of the probe lift-off. Based on simulation results, this paper presents that choosing proper probe excitation frequency can eliminate the disturbance of coil impedance caused by the lift-off, and obtains better results.

Fuzzy logic control of electrodynamic levitation devices coupled to dynamic finite volume method analysis

Electrodynamic levitation devices, which utilizing eddy currents induced in the levitated item to produce the repulsive force, are being involved in many engineering applications due to its fast response. This kind of repulsion is particularly used in electromagnetic launcher, electromagnetic brake and other applications. To analyze and improve the dynamic behavior and performances of such devices, the conventional way is using the finite element method (FEM), due to its ability of using adaptive mesh to handle complex geometries. Nevertheless, it has a serious limitation in efficiency for large number of variables which is reflected by the high cost in terms of computational properties. During the past few years, the finite volume method FVM formulations have gained attention inside the electromagnetic community. The method has been proved its effectiveness in the solution of different kinds of problems, such as in magnetostatic field computation and eddy current nondestructive testing. The FVM method is particularly attractive thanks to its small required storage memory and reduced CPU time. In this paper an FVM model is developed to analyze the dynamic characteristic of the motion of the electrodynamic levitation device TEAM Workshop Problem 28. The dynamic characteristic of the motion is obtained by solving the electromagnetic equation coupled to the mechanical one. The repulsive force applied to the levitated plate of TEAM Workshop Problem 28, is computed by the interaction between eddy current induced in the plate and the magnetic flux density. A comparison between experimental and numerical results is carried out to show the efficiency of the developed model. What’s more, based on the developed FVM model, a fuzzy logic controller FLC is designed and implemented to control the position of the levitated item.

Separability of Multiple Deep Crack Defects With an NDE Eddy Current System

Finite element simulation was carried out to characterize the separability of an eddy current nondestructive testing (EC-NDT) system in the case of the detection of deep cracks located in an aluminum plate. The interaction among multiple cracks was also analyzed. The results show that the separability of the EC system not only depends on the sensitive element dimensions but also on different parameters of the system, such as the sensor lift-off, the excitation frequency and the cracks depth. Furthermore, the simulations show that under some conditions, the magnetic response sensed for multiple cracks can be calculated as the superposition of the signals obtained for each crack. This point essentially simplifies the solution of the inverse problem of crack reconstruction.

A framework for multidimensional learning using multilabel ranking

Multidimensional learning problem which is a more general form of supervised machine learning dealing with learning a function that maps a vector of input features to a vector of class labels is discussed in this paper. The study focuses on a framework which uses multilabel ranking with an emphasis on solving multidimensional learning problems. Performance of the framework is evaluated using mean accuracy, global accuracy and entropy of accuracy. Studies using real world benchmark dataset have shown that all the assumptions made in building the framework are valid. In addition to this, studies are carried out on images from eddy current non-destructive testing of stainless steel plate having subsurface defects clearly confirm that the proposed framework is well suitable for learning multidimensional problems and is superior to uni-dimensional learning algorithms.

Quantitative Analysis of Transverse Cracking of Rail Using Eddy Current Non-Destructive Testing

In this study, we analyzed transverse cracks in rails using eddy current testing. Quantitative analysis was performed on transverse crack defect signals ascertained by actual flaw detection and finite element simulation of the rail that had developed a central transverse crack. The conductivity and width of the finite element model were equivalent to those of the actual rail. By analyzing the distribution of eddy current on the rail surface, the relationship between the variation in the density of eddy current and size of transverse crack was obtained. Experimental and simulation results indicated that the application of eddy current testing in quantitative analysis of a rail flaw was feasible and would be a valuable reference in future research.

Multi-frequency Eddy Current Testing using a GMR based instrument

The paper proposes the use of a suitable multi-frequency approach applied to a novel instrument for eddy current non-destructive testing on conductive materials. The instrument is composed by a smart eddy current probe, based on a Giant Magneto Resistance sensor, and by a suitable processing unit. Key features of the proposed instrument are the capability of detecting, locating, and characterizing thin defects such as superficial and sub-superficial cracks. The proposed multi-frequency solution, together with a suitable data processing, allow both the sensitivity in the defect detection to be increased and the ability to evaluate the defect characteristics in terms of shape and dimension to be improved.

Study on the Signal Processing Methods of the Eddy Current Testing on the Steel Ball Surface Defects

In the detection of steel ball surface defects, the non-destructive eddy current testing has the advantage of reliability, high efficiency, non-contact and easy to automate, etc. Base on the establishment of the mathematical model of the ball surface unfolding, Correct processing of eddy current sensor output signal is the first conditions of accuracy. In the paper, base on the propose of unfolding mechanism of the ball's surface, with smooth, phase-sensitive detection, filtering and other methods to process the detected signal, so interference signals can be suppressed and to improve the signal to noise ratio; Then the signal can be further analyzed in time domain or frequency domain, the signal amplitude, frequency, phase and other characteristic quantities can be extracted, and the characteristic quantity of defect signal can be Calculated, So the surface quality of steel balls can be determined.

펄스 와전류 비파괴검사의 원리

펄스 와전류 비파괴검사의 원리

An Improved Device of Detecting True and False Coin

In order to reduce the loss of an automatic coin machine for using a 1-Yuan false coin, an improved device is designed by applying the eddy current nondestructive testing technology, which uses two distinct frequency signals to detect the information of coin. The device includes four modules: two LC oscillating circuits, signal processing module, microprocessor module and external control circuits. The detection algorithm selects two counting values under two distinct frequency signals. The low frequency circuit detects the information of the coin material, and the high frequency circuit detects the feature of the coin surface. The detection region of true coin is decided by the method of least square. The experiment shows that the device has the characters of stability, reliability and high accuracy.

Solution to the problem of E-cored coil above a layered half-space using the method of truncated region eigenfunction expansion

In eddy current non-destructive testing, using ferrite cores with different shapes produce interesting and very important predictions on locating underneath cracks, corrosions and flaws in conductive layers. This paper describes a method of calculating magnetic vector potential for an E-cored eddy current probe above a layered half space. The coil impedance variation with frequency and eddy currents are determined. The results of the calculations are in very good agreement with results from COMSOL.

GMR-Based ECT Instrument for Detection and Characterization of Crack on a Planar Specimen: A Hand-Held Solution

This paper proposes a novel instrument for eddy-current (EC) nondestructive testing on conductive materials. The instrument is composed by a smart EC probe, based on a GMR sensor, and a suitable processing unit. The key features of the proposed instrument are the capability of detecting, locating, and characterizing thin defects as superficial and subsuperficial cracks. The main goal of the proposal is the realization of a very low cost instrument that is able to reach performance comparable with instrumentation available on the market and especially suited for application fields such as aerospace and maritime. In this paper, the steps followed for the instrument realization, together with its experimental characterization, are described in detail.

A New Type Device of Detecting True and False Coin

In order to reduce the loss of an automatic coin machine for using a 1-Yuan false coin, a new device is designed by applying the eddy current nondestructive testing technology. The device includes four modules: LC oscillating circuit module, signal processing module, microprocessor module and external control circuit module. The device identifies false coins by the frequency changing quantity of the circuit oscillation. For overcoming temperature drift, component aging and environment disturbance, the algorithm adopts the technology of background frequency updating and Median filter. The experiment shows the device has the character of stability, reliability and high accuracy.