A Truncated Logistic Regression Model in Probability of Detection Evaluation.

In nondestructive evaluation (NDE) studies, the probability of detection curve (POD) is an important performance metric. The traditional POD estimation is to conduct NDE inspections for artificially fabricated specimens with known flaws. This approach is often challenges because not only do fabricated flaws not adequately represent the flaws found in the field, but the cost and time of fabricating artificial specimens can also be very high. In practice, field samples and components in service with naturally occurring defects are readily available and much less expensive to test. However, the disadvantage of this field approach is that the exact number and sizes of the flaws in a sample (especially for flaws with small sizes) are unknown. As a result, serious bias in estimating the POD can occur. In this article, a truncated logistic regression method is developed that can estimate POD accurately and consistently with field samples based on multiple inspections. A case study illustrates the successful application of the proposed approach in a leading manufacturing company. The simulation studies also show that the proposed methods estimate the POD on field samples with quality comparable to that of the traditional approach on artificial specimens. [ABSTRACT FROM AUTHOR]