Classification of pulsed eddy current GMR data on aircraft structures

This paper presents a technique to automatically detect third-layer cracks at rivet sites in aircraft structures using the response signals collected by giant magneto-resistive (GMR) sensors. The inspection system uses pulsed waveform as the excitation source of a multi-line coil and captures the transient fields associated with the induced eddy currents via a GMR sensor, which was developed to detect cracking and corrosion in multi-layer aircraft structures. An automatic scan of the region around the rivet generates C-scan image data that can be processed to detect cracks under the rivet head. Using a 2-D image of each rivet head, feature extraction and classification schemes based on principal component analysis and the k-means algorithm have been successfully developed to detect cracks of varying size located in the third layers at a depth of up to 10 mm below the surface.     

Pulsed eddy current imaging and frequency spectrum analysis for hidden defect nondestructive testing and evaluation

Hidden defect characterisation in some complex structures is difficult. Pulsed Eddy Current (PEC) imaging based on rectangular excitation coil is investigated in this paper and hidden defect nondestructive testing and evaluation (detection, classification, and quantification) is carried out based on the various C-scan images. Experimental results have illustrated that hidden defects can be identified effectively by particular character in C-scan imaging results and sub-surface defects can be discriminated to correct class by selecting the rising time from response in time domain. The quantification information of hidden defects is preliminarily obtained based on the contour and 3D images. In addition, PEC imaging and frequency spectrum analysis are effective to detect, classify, and evaluate the sub-surface defects under the influence of edge effect of specimen. To sum up, PEC imaging is an effective approach to characterise hidden defects and sub-surface defects.     

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

Detailed experimental studies have been carried out for the determination of optimum eddy current excitation frequencies for the defects located at different depths below the top surface of an aluminum plate. These subsurface defects were detected by using a highly sensitive superconducting quantum interference device (SQUID) based eddy current non-destructive evaluation (NDE) system. The signal to noise ratio was found to be significantly higher at the optimum excitation frequency, which depended on the depth of the defect. The optimum excitation frequencies have been evaluated for defects located at different depths from 2 to 14 mm below the top surface of the plate. The defect depth was varied in steps of 2 mm, while the overall total thickness of the stack of plates was kept constant at 15 mm. Each defect represented a localized loss of conductor volume, which was 60 mm in length, 0.75 mm in width and 1 mm in height. The experimental results show that the square root of the optimum excitation frequency is inversely proportional to the depth of defect.     

Study on defect classification in multi-layer structures based on Fisher linear discriminate analysis by using pulsed eddy current technique

Pulsed eddy current (PEC) is an emerging non-destructive testing technique with wide application potential. In this study, defect parameter identification in multi-layer structures is examined by using the PEC technique, and a Fisher linear discriminate analysis (FLDA)-based defect classification method is proposed. Defect localization and shape identification are investigated, and defects on the surface and subsurface of the third layer are discriminated. The time domain characterization method is introduced and researched by using the peak time and zero-crossing time of PEC response signals, the principal component analysis algorithm and the FLDA method. The feature extraction results of the three methods are used as the input values of support vector machine for defect classification and feature extraction, and the classification methods are compared. Theoretical analysis and experimental results show that the proposed method can contribute to effective classification for defect parameter identification.     

Characterization of subsurface defects in aeronautical riveted lap-joints using multi-frequency eddy current imaging

The authors present an original eddy current imager (ECI) designed for the fast and accurate non-destructive evaluation of defects buried next to rivets in aeronautical lap-joints. The ECI is associated to a signal processing method based on a principal component analysis (PCA) followed by a maximum likelihood (ML) approach. The PCA was implemented using EC images obtained with selected excitation frequencies. These images are considered as resulting from a linear mixing of different sources including the presence of rivets and defects, and the PCA is used to separate these sources thanks to an eigen decomposition of the EC data covariance matrix. As a result, the defect signatures are enhanced and used to implement an automatic defect characterization. This characterization is carried out by the means of an ML approach which allows the length and depth of the defects to be estimated. The method was implemented for the evaluation of a laboratory made riveted lap joint mock-up featuring buried defects. It was experimentally optimized and successfully implemented for the characterization of calibrated defects ranging from 2 to 10 mm in length and 2 to 8 mm in depth.     

Dual frequency fusion for defect signal enhancement in EC-GMR inspecton of riveted multilayer structures

Reliable detection of defects under fasteners at rivet sites is one of the most critical problems in inspection of multilayer structures. Rotating eddy current (EC) excitation with giant magnetoresistive (GMR) sensor array field pick up (EC-GMR) has proved to be a promising approach in this application. In this paper, fusion of dual frequency is applied to EC-GMR scan data acquired with a novel differential array probe, to suppress the strong fastener response and hence improve the detectability of defects. Performance of the data fusion algorithm is evaluated on a two-layer aluminum sample with steel fasteners and notches introduced in the bottom layer using the electric discharge machine (EDM). Experimental results demonstrate that defects of different sizes can be successfully detected by evaluating the residual field after fusion. Defect orientation angle has minimal impact on the sensitivity of the probe owing to rotating eddy current excitation.     

Pulsed electromagnetic methods for defect detection and characterisation

The magnetic flux leakage (MFL) method has very good defect detection and location capabilities, but defect sizing capabilities, especially for sub-surface defect characterisation, are limited. The pulsed magnetic flux leakage (PMFL) technique has recently been introduced and shown to have great potential for automated defect sizing for surface-breaking defects using time-frequency signal processing techniques, but sizing of sub-surface defects has proved problematic. In this paper, pulsed magnetic reluctance (PMR), a new electromagnetic (EM) non-destructive evaluation (NDE) technique, is introduced and incorporated into a dual PMFL/PMR probe for the characterisation of surface and sub-surface defects in ferromagnetic materials. Experimental results from a comparison study of the two techniques using variety of defects analysed using time-frequency analysis show that the techniques offer complementary information, with PMFL providing defect location data and data for the characterisation of surface defects and PMR offering sub-surface defect characterisation capabilities. The work concludes that integration of these inspection techniques in the new pulsed EM probe can provide enhanced defect characterisation capabilities for flux leakage-based inspection systems using relatively simple time-frequency signal processing techniques.     

Pulsed eddy current signal processing method for signal denoising in ferromagnetic plate testing

A signal denoising method is presented for ferromagnetic material pulsed eddy current (PEC) testing signal. The method firstly performs multiple measurements, and obtains an averaged signal and then transforms the averaged PEC signal from Cartesian domain to double logarithmic domain by the developed algorithm. A median filtering process is then performed for double logarithmic domain signal. An invert transform is optional to be performed to transform the processed signal from double logarithmic domain back to Cartesian domain. A signal-to-noise ratio (SNR) improvement quantification method is applied to evaluate the SNR improvement. The performance of the method is then verified by experiments. The effects of signal averaging number and median filter’s order are discussed. Experiment results show the method is able to increase SNR by about 40 dB.     

Pulsed eddy current testing with variable duty cycle on rivet joints

In pulsed eddy current testing, repetitive excitation signals with different duty cycles have different spectral representations. This work studies the influence of duty cycle on the ability to detect holes and EDM notches beneath rivet heads in subsurface layers of stratified samples. Feature patterns for the integrity of rivet joints are proposed and verified. The proposed method has the added advantage in that no reference sample is needed while employing multiple pulse measurements, with different pulse widths. Experimental testing and modelling approaches are discussed in connection with defect depth quantification, which can be extended to the quantification of complex defects.     

Feature extraction and selection for defect classification of pulsed eddy current NDT

Pulsed eddy current (PEC) is a new emerging nondestructive testing (NDT) technique using a broadband pulse excitation with rich frequency information and has wide application potentials. This technique mainly uses feature points and response signal shapes for defect detection and characterization, including peak point, frequency analysis, and statistical methods such as principal component analysis (PCA). This paper introduces the application of Hilbert transform to extract a new descending feature point and use the point as a cutoff point of sampling data for detection and feature estimation. The response signal is then divided by the conventional rising, peak, and the new descending points. Some shape features of the rising part and descending part are extracted. The characters of shape features are also discussed and compared. Various feature selection and integrations are proposed for defect classification. Experimental studies, including blind tests, show the validation of the new features and combination of selected features in defect classification. The robustness of the features and further work are also discussed.     

Inspection of defects in conductive multi-layered structures by an eddy current scanning technique: Simulation and experiments

The forward problem of eddy current detection of defects by scanning conductive multi-layered structures is investigated and the change of the probe coil impedance is modeled by using finite element analysis method. Based on the ANSYS software a fast simulating program is developed and then the coil impedance changes due to the existing of defects in different lengths, shapes and at different locations in conductive multi-layers are calculated. An experimental eddy current testing system combined with a scanner is established and scanning testing experiments are carried out. The simulation and experimental results are compared. The agreement of them shows that the technique studied is promising and can help us to understand the probe responses and can be applied to the inversion model to determine the defect parameters in many important fields ranging from aerospace to energy and transportation industries.     

Pulse-modulation eddy current inspection of subsurface corrosion in conductive structures

Due to corrosive and hostile environment, in-service conductive structures are prone to subsurface corrosion which has posed a severe threat to structural integrity and safety. Although Pulsed eddy current testing (PEC) has been found advantageous over other Electromagnetic Non-destructive Evaluation (ENDE) techniques particularly in detection and characterisation of subsurface defects in conductive structures, it is subject to technical drawbacks. In light of this, in this paper, Pulse-modulation eddy current technique (PMEC) is proposed in an effort to enhance the inspection sensitivity to subsurface corrosion and quality of corrosion imaging. Closed-form expressions of PMEC responses to subsurface corrosion are formulated via the Extended Truncated Region Eigenfunction Expansion (ETREE) modelling. A series of simulations are subsequently conducted to analyse the characteristics of PMEC signals and inspection sensitivity. Following this, experiments of PMEC for evaluation and imaging of subsurface corrosion are carried out. Through theoretical and experimental investigation, it has been found that PMEC is advantageous over PEC in terms of evaluation sensitivity and quality of corrosion imaging.     

Pulsed eddy current thickness measurement using phase features immune to liftoff effect

Conventionally, peak value and peak time are extracted from pulsed eddy current (PEC) response as features for thickness measurement. However, they suffer from liftoff variations. In this work, the phase of spectral PEC response from a Hall sensor are proposed to serve as robust features for thickness evaluation. The presented novel features are immune to liftoff effect, because phase signals remain nearly constant against liftoff variations. An analytical model was formulated, and simulations were performed to uncover the physics of the characteristics of the phase feature and establish the relationship between the phase feature and sample thickness. Experiments were carried out to validate the proposed phase feature. Eventually, the proposed phase feature was evaluated for accurate thickness measurement and some key factors were discussed.     

Pulsed eddy current testing of ferromagnetic specimen based on variable pulse width excitation

Pulsed eddy current testing (PECT) is a rapidly developing technology which has wide potential applications. For the PECT system which uses detection coils, a no-reference-needed and more efficient method, for quantifying the wall thickness of the ferromagnetic specimen, should be found. In this paper, a kind of variable pulse width excitation is proposed. Based on the excitation, the slope that the relative increment of magnetic flux linear decays with the increase of pulse width in the semi-logarithmic domain is found to be an effective and no-reference-needed feature. First, the analytical expression for the relative increment of magnetic flux is presented, and the validity of the feature is verified by experiments. Then the potential factors affecting the feature are investigated in detail. Results show that when the electromagnetic properties of the specimen are invariant, the feature is independent of pulse width parameters, analysis interval and coatings thickness. At last, a quantitative method is demonstrated. More time could be saved for the narrow pulse comparing it with the existing excitations, and the feature could widely meet engineering applications.     

主成分分析法在脉冲涡流缺陷识别中的应用

在钢结构脉冲涡流缺陷识别中,通常采用信号的峰值幅度、过零时间、主峰面积等特征参数对缺陷进行表征。但上述参数相互关联,存在一定的信息冗余,增加了数据分析量及信息筛选难度,进而影响了缺陷识别的效率。针对上述问题,采用主成分分析法对脉冲涡流信号的6个特征参数进行降维处理,构造了一个主成分特征,减少了信息冗余;将上述主成分特征输入Logistic分类器,实现了对钢结构减薄缺陷的准确识别。结果表明:主成分分析法可以在确保缺陷识别准确率的情况下,有效减少分类器处理的数据量,提高缺陷识别效率。     

Design of eddy current-based dielectric constant meter for defect detection in glass fiber reinforced plastics

This paper presents the design of an eddy current testing probe for inspection of non-conductive glass fiber reinforced plastics. Because the magnetic field contains information pertaining to the permittivity of materials under test, eddy current testing offers the possibility of flaw detection in non-conductive materials through detection of the difference in permittivity between the intact part and the defective part of each material. We analytically investigated the design of a probe suitable for dielectric constant measurements. Experimental studies proved that the proposed probe can detect slit defects and flat-bottomed holes located 2 mm away from the surface of the glass fiber reinforced plastic samples.     

A detecting method for spherical fuel elements in pebble-bed HTGR using eddy current detection

In pebble-bed high temperature gas-cooled reactors (HTGRs), spherical fuel elements move inside pipelines of a handling system, which should be controlled precisely. A detecting method for these elements is proposed in this paper, which is achieved by a detecting system with self-diagnosis function. Detecting signals are obtained by sensors installed outside pipes. A signal identification algorithm was designed for graphite ball detection. Electromagnetic simulations and detecting experiments were performed for system optimization and development of the method. The results show that the proposed method is capable for spherical fuel elements detection, and can be successfully used in practical application.