Abstract

The purpose of this study is to develop an artificial intelligent eddy current crack detection system in collaboration with 6-Degree-of-Freedom (DOF) robotic arm in order to provide the end users with reliable crack information.
In this particular study, the main focus is on data fusion which includes signal filtering, signal feature extraction, feature recognition, and final decision making. Various features such as the amplitude, phase angle and width of the loop from the measured differential eddy current test (ECT) signals are extracted to represent the changes of the electrical impedance of the ECT probe due to crack presence. Furthermore, a data base has been built for the extracted features from the known notch cracks purchased from Olympus. An adaptive neuro-fuzzy inference engine is trained to map the complex and nonlinear relationship between the extracted features and the crack information. The experimental tests show that not only the developed intelligent system is able to extract signal features and provide the user with 1. defect presence, 2. predict the depth of unknown crack based on the trained fuzzy logic engine. In addition, in terms of experimental setup, a data acquisition system implemented with 6 DOF robot arm for ECT is established. Extra works such as coordinate calibration, prototype probe holder design, on-line crack position location detection has also been carried out.