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Eingeladener Vortrag
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Non-destructive testing for surface crack detection and head check depth quantification at the gauge corner of railway tracks can be achieved using eddy current methods. With the extension of the tested zone to the running surface, rail defect signal types other than head checks can be measured. Due to their mostly irregular shape, a quantitation based on a calibration against regular test cracks of varying depth may not be linear. Estimates of the expected influence of more complex crack patterns may be obtained by a finite element simulation of sufficiently simple limiting cases, like two displaced or intersecting cracks or a simply branched or flexed crack. As a first step, a 3D finite element model of the HC10 eddy current probe distributed by Prüftechnik Linke und Rühe (PLR), Germany was built and verified against measured results from an (easily fabricated) reference block with isolated long cracks.
Increased speed, heavier loads, altered material and modern drive systems result in an increasing number of rail flaws. The appearance of these flaws also changes continually due to the rapid change in damage mechanisms of modern rolling stock. Hence, interpretation has become difficult when evaluating non-destructive rail testing results. Due to the changed interplay between detection methods and flaws, the recorded signals may result in unclassified types of rail flaws. Methods for automatic rail inspection (according to defect detection and classification) undergo continual development. Signal processing is a key technology to master the challenge of classification and maintain resolution and detection quality, independent of operation speed. The basic ideas of signal processing, based on the Glassy-Rail-Diagram for classification purposes, are presented herein. Examples for the detection of damages caused by rolling contact fatigue also are given, and synergetic effects of combined evaluation of diverse inspection methods are shown.
Bei der zerstörungsfreien Prüfung verlegter Eisenbahnschienen werden die Rohdaten derzeit in proprietären Datenformaten gespeichert und auf Datenträgern zwischen den Prüfzügen und den auswertenden Stellen versendet. Die proprietären Datenformate sind in der Regel nur den Herstellern der Prüfsysteme bekannt und deren Dokumentation nicht allgemein zugänglich.
Die „Standard Practice for Digital Imaging and Communication in Nondestructive Evaluation“ (DICONDE), basierend auf dem medizinischen Standard „Digital Imaging and Communication in Medicine“ (DICOM), ermöglicht es, sowohl Prüfdaten als auch Prüfergebnisse und Streckeninformationen in einem standardisierten Format zu speichern und zwischen verschiedenen Endpunkten zu übertragen.
Das Poster gibt zunächst einen kurzen Überblick über die hierarchische Struktur von DICONDE und zeigt dann, wie DICONDE bei der Prüfung verlegter Eisenbahnschienen verwendet werden kann. Die geometrischen Besonderheiten (mehrere Kilometer Länge pro Prüffahrt, kurviger Streckenverlauf) stellen dabei eine besondere Herausforderung dar. Im Rahmen des mFUND-geförderten Projektes „Arteficial Intelligence for Railway Inspection (AIFRI)“, Förderkennzeichen 19FS2014C, wurde ein Vorschlag für eine Erweiterung des DICONDE-Standards für die Schienenprüfung erarbeitet und bei der ASTM eingereicht.
Human factors (HFs) are a frequently mentioned topic when talking about the reliability of non-destructive testing (NDT). However, probability of detection (POD), the commonly used measure of NDT reliability, only looks at the technical capability of an NDT system to detect a defect.
After several decades of research on the influence of HFs on NDT reliability, there is still no commonly accepted approach to rendering HFs visible in reliability assessment. This paper provides an overview of possible quantitative and qualitative methods for integrating HFs into the reliability assessment. It is concluded that reliability assessment is best carried out using both quantifiable and non-quantifiable approaches to HFs.
Automated Wall Thickness Evaluation for Turbine Blades Using Robot-Guided Ultrasonic Array Imaging
(2024)
Nondestructive testing has become an essential part of the maintenance of modern gas turbine blades and vanes since it provides an increase in both safety against critical failure and efficiency of operation. Targeted repairs of the blade’s airfoil require localized wall thickness information. This information, however, is hard to obtain by nondestructive testing due to the complex shapes of surfaces, cavities, and material characteristics. To address this problem, we introduce an automated nondestructive testing system that scans the part using an immersed ultrasonic array probe guided by a robot arm. For imaging, we adopt a two-step, surface-adaptive Total Focusing Method (TFM) approach.
For each test position, the TFM allows us to identify the outer surface, followed by calculating an adaptive image of the interior of the part, where the inner surface’s position and shape are obtained. To handle the large volumes of data, the surface features are automatically extracted from the TFM images using specialized image processing algorithms. Subsequently, the collection of 2D extracted surface data is merged and smoothed in 3D space to form the outer and inner surfaces, facilitating wall thickness evaluation. With this approach, representative zones on two gas turbine vanes were tested, and the reconstructed wall thickness values were evaluated via comparison with reference data from an optical scan. For the test zones on two turbine vanes, average errors ranging from 0.05 mm to 0.1 mm were identified, with a standard deviation of 0.06–0.16 mm.
Die zerstörungsfreien Methoden zur Untersuchung und Charakterisierung von Materialien sowie zur Detektion von betriebsbedingten und herstellungsbedingten Fehlern mit akustischen und elektrischen Methoden werden vorgestellt. Schwerpunkt bilden die Ultraschall-, die Wirbelstrom- und die Streuflussprüfung. Die Verfahren werden anhand des Aufgabenspektrums des Fachbereiches 8.4 der BAM dargestellt.
Since 1999 there has been a continuous development of non-destructive head check inspection. Especially in case of modern rail inspection systems, high demands on a fast, detailed detection and classification of defects called for improved testing methods. Due to the large amount of different rail types and profiles, the adaptation and optimization of algorithms is still in progress. Additional data analysis is under devel-opment for combined eddy current and ultrasonic inspection methods. This presentation gives an over-view of further enhancement and linking of testing systems as well as data processing. Main foci of the development are an improved sensitivity and an increased reliability of the testing results. Also, addi-tional information can be obtained like determination of local hardness and roughness of the rail as well as an enhanced resolution for locating of defects which will be part of the presented work.
Increased speed, heavier loads, altered material and modern drive system concepts result in an increasing number of flaws in railways. Caused by the rapid change in damage mechanism by modern rolling stock the appearance of the flaws also alters. Hence, interpretation of non-destructive rail testing results may become difficult. Caused by the changed interplay between detection method and flaw the recorded signals will result in an unknown type for the rail flaws type classification.
Methods for automatic rail inspection according to defect detection and classification have been developed continuously. Signal processing is a key technology to master the challenge of classification and maintain resolution and detection quality independently of operation speed.
The basic ideas of signal processing based on the Glassy-Rail-Diagram for classification purposes will be presented. Examples for the detection of damages caused by rolling contact fatigue are given. Synergetic effects of combined evaluation of diverse inspection methods are shown.
Non-destructive testing for surface crack detection and head check depth quantification at the gauge corner of railway tracks can be achieved using eddy current methods. With the extension of the tested zone to the running surface, rail defect signal types other than head checks can be measured. Due to their mostly irregular shape, a quantitation based on a calibration against regular test cracks of varying depth may not be linear. Estimates of the expected influence of more complex crack patterns may be obtained by a finite element simulation of sufficiently simple limiting cases, like two displaced or intersecting cracks or a simply branched or flexed crack. As a first step, a 3D finite element model of the HC10 eddy current probe distributed by Prüftechnik Linke und Rühe (PLR), Germany was built and verified against measured results from an (easily fabricated) reference block with isolated long cracks.