Automatic detecting and classifying defects during eddy current inspection of riveted lap-joints

Uppsala University, Signals and Systems, SE-751 20 Uppsala, Sweden
NDT & E International (Impact Factor: 1.74). 01/2000; DOI: 10.1016/S0963-8695(99)00007-9

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: This paper investigates the application of two source separation techniques, principal component analysis and independent component analysis, to process the data from the inspection of riveted lap joints by eddy currents. An eddy current array sensor is designed for the rapid inspection of lap-joints and used to test a set of flawed rivet configurations featuring 1–10mm notches, buried down to a 4mm depth. Implementation methods are proposed for processing such eddy current data by means of both the considered source separation techniques. The signal processing results obtained from the experimental data are compared in terms of source separation efficiency and detection using a receiver operating characteristic approach. In the light of this study, both the techniques appear to be efficient. However, the principal component analysis provides better defect detection results, especially for deeply buried defects.
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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.
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