An electromagnetic multi-parameter strategy to detect faults in anchor rods using neural networks

Abstract

Network parameters are capable of conveying information about the constitution of a medium in which a highfrequency wave propagates. In this paper, this characteristic is exploited in order to design a nondestructive system to detect corrosion in anchor rods of guyed towers. A compound database is built with the simulated and measured parameters return loss, input impedance, and voltage standing wave ratio for normal and faulty rods examples. Artificial neural networks are used to capture underlying characteristics of data and establish relationships between these parameters and the presence of corrosion in the rods, without the need for physical models. Experimental results show that the innovative use of a multiparameter strategy achieves a high accuracy and enhances the detection capacity of the system.