Defect deconvolution using 3rd order statistics for Ultrasonic Nondestructive Testing

Abstract

Ultrasonic nondestructive testing (NDT) is primarily based upon the detection and classification of a defect in the field of industrial materials. This information is useful in making administrative decisions in terms of maintenance and replacement. The technique presented in this paper utilizes the concept of defect induction as a convolution process between the clean sample and the defect signature. Hence, to identify the type of defect a deconvolution approach can be useful. Due to several similarities between the ultrasonic echoes and the usual modulated sinusoids, a motivation is present to use 2nd and higher order statistics for completely defining the waveform. Such a definition, when compared with standard defects, will provide useful insight in terms of defect classifications and understanding.