Automated classification of eddy current signatures during manual inspection

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.