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Reliability analysis of the ultrasonic inspection system for the inspection of hollow railway axles
(2016)
Axles are safety critical train components that are subjected to significant cyclic loading during operation. If the crack is initiated in the axle, cyclic loading will lead to crack propagation. To maintain structural integrity, axles must be periodically inspected for fatigue cracks in the material. Deutsche Bahn uses mechanized ultrasonic inspection system to inspect hollow railway axles. The inspections are performed from the bore surface, using several conventional transducers with different incident angles, inspecting the axle along the entire length. As with the every safety critical system, the reliability of these inspections must be determined with regard to their flaw detection capabilities. Traditionally this is done according to the relevant standards for railway vehicles. To investigate the capability of the NDT system more thoroughly, we want to evaluate the capability of the inspection system to detect flaws by means of probability of detection (POD) curves. It will be shown that other parameters, beside the size of the crack, for example crack position in the axle, influence the detection of the crack. The influence of these parameters was evaluated using ultrasonic simulation. The evaluation served as an input for the manufacturing of the flaws in the real scale axle. Once these axles are inspected and the data evaluated, using data from both measurement and simulation, we will express the POD of the crack as function of influencing parameters using the multiparameter POD model.
Defects in FRP structures may be introduced during the processing and fabrication of composite components and can initiate or grow in-service. In the context of this JRP, the term ‚defect‘ refers to imperfections introduced during manufacture/processing and/or secondary machining operations, as well as damage sustained during a component‘s service life. One of the challenges facing accurate and repeatable defect detection in FRP composites is the multitude of defect types that exist, each with characteristics that present different challenges to the NDE practitioner. In order for a particular NDE technique to achieve broad acceptance by industry, it is desirable for the technique to be able to detect a range of defect types with a high level of confidence. Project VITCEA (Validated Inspection Techniques for Composites in Energy Applications) is developing and validating traceable procedures for novel NDE techniques with contrasting detection capabilities, which will underpin the increased use of FRP composites for improved efficiency and reliability in energy related applications e.g. wind and marine turbine blades, nacelles, oil and gas flexible risers.
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.