Highlighting anomalies in ultrasonic scan data by Shannon Information processing

Analysis of ultrasonic nondestructive testing data can be viewed as an exercise in anomaly detection, whereby, signals/image features found to appear consistently throughout a scan are attributed to non-relevant reflections from the weld root/cap, back-wall, wedge base, etc., while isolated indications of sufficient amplitude (with respect to a calibrated reference level) are identified as potential defects requiring further characterization. Finding indications which correspond to flaws in this way can be challenging, particularly when they are low amplitude and/or are proximate to strong geometric reflectors. In this paper, a novel post-processing scheme is proposed to facilitate the identification of defects in encoded ultrasonic scans. The algorithm effectively emphasizes indications which are unusual compared with an un-flawed reference scan (or region of a scan). This is accomplished by estimating the distribution of signal amplitudes in the reference scan and then using the reference distribution to compute the so-called Shannon Informationassociated with new data points. Shannon Information is a fundamental metric in Information Theory, which quantifies how surprised one should be to observe a given quantity - ultrasonic signal amplitude in this case - knowing how that quantity varies in general, as described by its probability distribution. The mathematical foundations of the technique are outlined in detail followed by a demonstration of the efficacy of Shannon Information processing on Time of Flight Diffraction (ToFD) and Phased Array (PA) scans featuring flaws which are difficult to discern by conventional means. The proposed method of presenting ultrasonic inspection data is observed to increase signal-to-noise ratio and highlight subtle perturbations in consistent, non-relevant scan features associated with the presence of defects. In addition, in the absence of flaws, Shannon Information processing is shown to transform ultrasonic signals, projection views and Delay and Sum rendered images into stationary random processes with known null distributions which allows for global detection thresholds to be set according to a desired level of statistical significance.

Automated summary

The analysis of ultrasonic nondestructive testing data involves identifying anomalies in the scan and differentiating them from potential defects. A novel post-processing method is proposed, which emphasizes unusual indications compared to a reference scan. This method increases signal-to-noise ratio, highlights subtle perturbations, and allows for global detection thresholds to be set.