<p>Non-destructive testing methods are used largely in component manufacturing industries like Aerospace, Renewables and Power to evaluate the properties of a material or the quality of a component by inspecting for cracks and discontinuities without causing damage to the part. Among the many non-destructive testing methods, Eddy current technique is one of the extensively used technique for inspecting electrically conductive material. Leveraging eddy current array sensors could potentially augment the inspection with images showing surface and sub-surface (within the skin depth) quality of part under investigation. Inspection using eddy current array probes improve the speed of inspection as wider area can be covered in any given time compared to using a single probe. However, in typical eddy current inspection, there can be significant amount of false calls arising from variation in lift-off and surface anomalies. Discrimination of defect signals from false calls can be very challenging. Conventionally, phase and amplitude information from each sense coil are used to discern defect indications from false calls. This paper describes a method to reduce false calls by using a wavelet based denoising algorithm and combining it with image-based features extracted from within a sliding window in the time domain to efficiently identify the crack from lift-off and surface variations. The results are verified on specimens with cracks of different sizes that are oriented randomly, along with locations available for baseline noise measurements.</p>

Reduced False Calls in Eddy Current Images Using Signal Processing

Paper Proceedings: https://asmedigitalcollection.asme.org/qnde/QNDE2021/volume/85529

ASME 2021 48th Annual Review of Progress in Quantitative Nondestructive Evaluation (QNDE2021)

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