AUTOMATED ANALYSIS OF EDDY CURRENT (EC) GIANT MAGNETO RESISTIVE (GMR) DATA

Eddy current testing (ECT) has been widely used in aircraft inspection. However ECT is limited by skin depth in detecting defects in multi‐layered complex structures. Advanced electromagnetic sensing methods, such as giant magnetoresistive (GMR) sensors offer much higher sensitivity that allow detection of defects located deep in the test object. Traditional magnitude based GMR signal interpretation methods using symmetry of images developed for detecting cracks under titanium fastener heads are ineffective when analyzing data from steel fastener heads which results in reduced probability of detection (POD). This paper presents a signal processing algorithm based on mixing the in‐phase and quadrature components of the GMR sensor signal via an optimum detection angle (ODA). Features derived from the mixed signal are clustered for automated crack detection using the MR sensor data. Performance of the algorithm is evaluated using signal to noise ratio (SNR) that measures efficacy of different features.