TITLE

Detection and classification of defects in ultrasonic NDE signals using time-frequency representations

AUTHOR(S)
Qidwai, Uvais; Costa, Antonio H.; Chen, C. H.
PUB. DATE
May 2000
SOURCE
AIP Conference Proceedings;2000, Vol. 509 Issue 1, p717
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
The ultrasonic wave, generated by a piezoelectric transducer coupled to the test specimen, propagates through the material and part of its energy is reflected when it encounters an non-homogeneity or discontinuity in its path, while the remainder is reflected by the back surface of the test specimen. Defect echo signals are masked by the characteristics of the measuring instruments, the propagation paths taken by the ultrasonic wave, and are corrupted by additive noise. This leads to difficulties in comparing and analyzing signals, particularly in automated defect identification systems employing different transducers. Further, the multi-component nature of material defects can add to the complexity of the defect identification criteria. With many one-dimensional (1-D) approaches, the multi-component defects can not be detected. Another drawback is that these techniques are not very robust for sharp ultrasonic peaks especially in a very hazardous environment. This paper proposes a technique based on the time-frequency representations (TFRs) of the real defect signals corresponding to artificially produced defects of various geometries in metals. Cohen’s class (quadratic) TFRs with Gaussian kernels are then used to represent the signals in the time-frequency (TF) plane. Once the TFR is obtained, various image processing morphological techniques are applied to the TFR (e.g. region of interest masking, edge detection, and profile separation). Based on the results of these operations, a binary image is produced which, in turn, leads to a novel set of features. Using these new features, defects have not only been detected but also classified as flat-cut, angular-cut, and circular-drills. Moreover, with some modifications of the threshold levels of the TFR kernel design, our technique can be used in relatively hostile environments with SNRs as low as 0 dB. Another important characteristic of our approach is the detection of multiple defects. This consists...
ACCESSION #
6029058

 

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