Abstract
For estimating the probability of detection (POD) in non-destructive evaluation (NDE), there are two standard methods, the so-called â versus a approach and the hit/miss approach. The two approaches have different requirements for the quality and quantity of input data as well as for the underlying NDE method. There is considerable overlap between the methods, and they have different limitations, so it is of interest to study the differences arising from using each methodology. In particular, if the dataset is not ideal, the methodologies may exhibit different problems dealing with various limitations in the data. In this paper, a comparison between â versus a and hit/miss analysis was completed for two different data sets, a manual aerospace eddy-current inspection and a nuclear industry phased array ultrasonic weld inspection using a simplified online tool. It was found that the two standard methods (â vs. a and hit/miss) may give significantly different results, if the true hit/miss decision is based on inspector judgement and not automated signal threshold. The true inspector hit/miss performance shows significant variance that is not attributable to signal amplitude. Model-assisted POD was not able to model the inspector performance due to lack of representative amplitude threshold and difficulties in capturing true signal variance. The paper presents experience from practical cases and may be considered a European viewpoint.
Highlights
The best practices of estimating probability of detection (POD) in non-destructive evaluation (NDE) are well established
The methods have recently been standardized by ASTM [8, 9] and these standards are congruent with the current MIL-HDBK methodology
The resulting best-fit and confidence limit lines are compared to the set detection threshold and the corresponding POD curves computed
Summary
The best practices of estimating probability of detection (POD) in non-destructive evaluation (NDE) are well established. The system has noise both on the signal (â varies due to factors other than a), which results in noisy â versus a relation. The task is to find a decision threshold (â value), that minimizes false calls from the noise and, in parallel, maximizes the number of cracks found (i.e. cracks with â above the threshold), given the variation in the â versus a relation. The ASTM-E3023 (and MIL-HDBK) solve this by fitting a linear function through the â versus a data, computing prediction intervals to take the notice and statistical uncertainty into account. The resulting best-fit and confidence limit lines are compared to the set detection threshold and the corresponding POD curves computed. Improvements to the classical Berens [10] model have been proposed to behave better with very limited data sets, e.g. by Syed Akbar Ali et al [11], Syed Akbar Ali and Rajagopal [12] and Le Gratiet et al [13]
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