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Nondestructive testing (NDT) is routinely used in the nuclear, rail, aerospace and automotive industries to search for flaws in components. A signal from the flaw, recorded by the NDT device, will vary from measurement to measurement. The sources of this innate signal variation can be categorized into intrinsic, human and application factors. This variation, especially when searching for flaws that are at the limits of the NDT detection capabilities, can result in a failure to detect a flaw. If the inspected components are safety critical, the capability of NDT system to find flaws must be determined in order to avoid the catastrophic consequences of a missed flaw. The NDT system capability to detect flaws is expressed in terms of reliability. The probability of detection (POD) curve is a widespread tool to quantify the reliability of NDT. The POD is determined by series of experiments on specimens containing a range of flaws with known characteristics. The production of a sufficient number of these flaws is time consuming and expensive. In this paper, a multi-parameter POD model that uses both simulation and experimental measurements to calculate the POD curves will be presented. Simulation is used to assess the intrinsic capability of the NDT system and the variability in the system is estimated from experimental measurements. The POD calculated with the multi-parameter model is more comprehensive than the one calculated with the traditional model and the number of costly experiments needed is reduced.
Deutsche Bahn periodically inspects hollow railway axles for fatigue cracks on the outer surface with mechanized ultrasonic inspection systems. According to the current standard for the inspection of railway axles, the capability of the inspection system to detect these cracks has to be demonstrated on the saw-cut type artificial defects. However, the geometry and the ultrasonic response of the real cracks that can occur on the outer surface of the axle are different from the saw-cut. Furthermore, it is demonstrated that the position and the orientation of the cracks are also important factors that influence the crack detectability. It is proposed to evaluate the influence of all factors on the detection of the cracks using the multi-parameter reliability model. The model uses numerical simulation and experiments to comprehensively address the influence of several factors on the probability of detection.