Hole Identification System in Conducting Plates by using Wavelet Networks
In this paper, we propose a wavelet network (WN) approach to the identification of holes in conducting plates, in the context of a Non Destructive Evaluation (NDE) signal processing system, based on the eddy currents inspection. The system aims to locate holes in the specimen under inspection by using a two-stage approach, namely, a WN followed by a least squares post-processing block. The WN stage estimates the distances between the hole and the sensor probes; the least squares stage identifies the hole on the basis of the distances computed by the previous neural block. The efficacy of the proposed approach is tested on artificial data and compared with different approaches based on feedforward multilayer perception (MLP) and on radial basis function neural network. The robustness of the system has been tested: the effects of the white noise and of the lift-off noise at different signal-to-noise ratios have been inspected.
KeywordsGlobal Position System Radial Basis Function Neural Network Wavelet Neural Network Neural Architecture Wavelet Network
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