Non-destructive testing of cracks using eddy-currents and a generalized regression neural network (GRNN)

• Published in: IEEE Antennas and Propagation Society International Symposium. Digest. Held in conjunction with: USNC/CNC/URSI North American Radio Sci. Meeting (Cat. No.03CH37450) Vol. 2; pp. 239 - 242 vol.2
• Main Authors: Bahramgiri, M., Barkeshli, K.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2003
• Subjects: Eddy currents; Finite element methods; Impedance; Magnetic noise; Neural networks; Noise shaping; Nondestructive testing; Probes; Robustness; Shape
• ISBN: 9780780378469; 0780378466
• DOI: 10.1109/APS.2003.1219222

In this paper, we propose a new method for the robust estimation of crack dimensions. The method is based on the eddy current evaluation and a generalized regression neural network (GRNN) scheme. The network is trained by several known crack shapes based on the input impedance of a magnetic probe using a finite element solution for the eddy currents. The target value to be trained was the shape of the crack using a window based on the probe impedance. Noisy data, added to the probe measurements, is used to enhance the robustness of the method. We present a comparison of the results obtained using the proposed method with those obtained from a feed-forward neural network. It is shown that the GRNN is faster both in training as well as in identification of the cracks.