دانلود Defect Characterization With Eddy Current Testing Using Nonlinear-Regression Feature Extraction and Artificial Neural Networks

ترجمه فارسی Defect Characterization With Eddy Current Testing Using Nonlinear-Regression Feature Extraction and Artificial Neural Networks
قیمت : 255,000 ریال
شناسه محصول : 2008712
نویسنده/ناشر/نام مجله : IEEE, Transactions on Instrumentation and Measurement
سال انتشار: 2013
تعداد صفحات انگليسي : 8
نوع فایل های ضمیمه : Pdf+Word
حجم فایل : 1 Mb
کلمه عبور همه فایلها : www.daneshgahi.com
عنوان انگليسي : Defect Characterization With Eddy Current Testing Using Nonlinear-Regression Feature Extraction and Artificial Neural Networks

چکیده

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.

Keywords: Artificial neural networks (ANNs) defect param- eter estimation eddy current testing
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