Automatic detecting and classifying defects during eddy current inspection of riveted lap-joints
ABSTRACT This article presents a novel method for automatic detection and classification of cracks located in the second lower layer of the aircraft lap-joints during Eddy Current (EC) inspection. The cracks originating from the rivet holes were detected using a tailor-made deep penetrating EC probe. The proposed method consists of three steps: pre-processing, feature extraction and classification. The pre-processing, performed before the feature extraction included median filtering, rotation and de-biasing of the EC patterns. The rotation of the patterns was performed so that energy of the responses to the rivets was maximized along the quadrature direction, while the defect responses were maximized in the in-phase direction in the impedance plane. Feature extraction was then performed using four different methods: discrete wavelet transform, Fourier descriptors, principal component analysis (PCA) and block mean values. The classification was performed using a standard multi-layer perceptron (MLP) neural network. All the pre-processing methods showed similar classification performance on the used data set, but the PCA method compressed the data best.
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ABSTRACT: 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.IEEE Transactions on Instrumentation and Measurement 05/2013; 62(5):1207. · 1.71 Impact Factor
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ABSTRACT: This article presents a novel method for automatic evaluation of flaws during a manual eddy current (EC) inspection procedure. In manual scanning, a signal related to impedance change in the complex plane is affected by large and unpredictable variations of scanning speed and alterations of probe position. This paper introduces a robust EC signal normalization method using non-linear filtration based on evaluation of the distance between consecutive samples in the complex plane and median based tracking of the EC signature. Feature extraction was performed using normalized Fourier and complex discrete wavelet descriptors. The classification was performed using six different methods: nearest-mean classifier, k-nearest neighborhood classifier, the standard multi-layer feed forward neural network with backpropagation, radial basis network, support vector machines and the adaptive-network-based fuzzy inference system. The method was tested on a single frequency instrument with an absolute probe and a dual frequency instrument with a probe for fast testing of rivets in layered structures. The results obtained using this approach demonstrate the effectiveness of the proposed system.NDT & E International 09/2005; DOI:10.1016/j.ndteint.2004.12.004 · 1.72 Impact Factor
- Advances in Imaging and Electron Physics 127:1-57. DOI:10.1016/S1076-5670(03)80096-4 · 0.58 Impact Factor