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Im DIN-Normenausschuss Materialprüfung (NMP) wurde das Normungsgremium NA 062-08-28 AA „Qualifizierung von zerstörungsfreien Prüfungen“ reaktiviert. Das Ziel der Reaktivierung ist die Erarbeitung einer Norm für einen standardisierten Prozess zur probabilistischen Bewertung von Prüfverfahren.
In der zerstörungsfreien Prüfung (ZfP) führt die Ermittlung von Mess- / Prüfunsicherheiten von Prüfverfahren, in normativer Hinsicht in Deutschland und Europa, noch ein Schattendasein. Die Norm ISO IEC 17025 und auch der VDA-Band 5 wurden weiter in Richtung Messunsicherheit entwickelt, allerdings führt ihre Anwendung in der ZfP zu Problemen.
Der Arbeitsausschuss NA 062-08-28 AA soll hier den Brückenschlag zwischen Messunsicherheit, objektiver statistischer Bewertung für ZfP-Verfahren und eine weitere Stärkung der probabilistischen Bruchmechanik erreichen. Geplant ist daher die Erarbeitung einer DIN-Norm, welche die Kriterien für eine korrekte Versuchsplanung, ein praktikables Vorgehen zur Bewertung der Prüfergebnisse und die mathematische Überführung in eine Zuverlässigkeitsaussage enthält. Es soll ein Leitfaden erstellt werden, der für ein möglichst breites Branchenspektrum eingesetzt werden kann, unabhängig von den Besonderheiten verschiedener Industriezweige.
Dargestellt werden das relevante Normungsgremium sowie der Hintergrund des zu erstellenden Dokuments.
Vortrag über die Berechnung von Messunsicherheiten und die POD-Analyse zur Aufbereitung von ZfP-Ergebnissen für die Zuverlässigkeitsbewertung bestehender Bauwerke.
The new crack luminescence method offers the possibility of making fatigue surface cracks in metallic materials more visible during inspections through a special coating system. This coating system consists of two layers, whereby the first layer has fluorescent properties and emits visible light as soon as it is irradiated by UV light. The top layer is black and is designed to prevent the fluorescent layer from emitting if no crack develops in the underlying material. The technique proved particularly useful in a wide variety of fatigue tests of steel components under laboratory conditions. Moreover, it has the potential to be used in various industrial applications. To enable industrial deployment and integration into maintenance strategies, a concept study is developed in this contribution, resulting in a qualification framework that can serve as a foundation for determining the reliability of the crack luminescence system in terms of a probability of detection curve. Within this study, factors causing measurement variability and uncertainty are being determined and their influences assessed. Due to the extension of the system by a moving computer vision system for automated crack detection using artificial intelligence, additional long-term effects associated with structural health monitoring systems need to be incorporated into an extended probability of detection study as part of the technical justification. Finally, important aspects and findings related to design of experiments are discussed, and a framework for reliability assessment of a new optical crack monitoring method is presented, emphasizing the influence of various uncertainty parameters, including long-term effects such as system ageing.
The new crack luminescence method offers the possibility of making fatigue surface cracks in metallic materials more visible during inspections through a special coating system. This coating system consists of two layers, whereby the first layer has fluorescent properties and emits visible light as soon as it is irradiated by UV light. The top layer is black and is designed to prevent the fluorescent layer from emitting if no crack develops in the underlying material.
The technique proved particularly useful in a wide variety of fatigue tests of steel components under laboratory conditions. Moreover, it has the potential to be used in various industrial applications. To enable industrial deployment and integration into maintenance strategies, a concept study is developed in this contribution, resulting in a qualification framework that can serve as a foundation for determining the reliability of the crack luminescence system in terms of a probability of detection curve. Within this study, factors causing measurement variability and uncertainty are being determined and their influences assessed. Due to the extension of the system by a moving computer vision system for automated crack detection using artificial intelligence, additional long-term effects associated with structural health monitoring systems need to be incorporated into an extended probability of detection study as part of the technical justification.
Finally, important aspects and findings related to design of experiments are discussed, and a framework for reliability assessment of a new optical crack monitoring method is presented, emphasizing the influence of various uncertainty parameters, including long-term effects such as system ageing.
Performance assessment for GuidedWave (GW)-based Structural Health Monitoring (SHM) systems is of major importance for industrial deployment.
With conventional feature extraction methods like damage indices, pathbased probability of detection (POD) analysis can be realized. To achieve reliability quantification enough data needs to be available, which is rarely the case.
Alternatives like methods for performance assessment on system level are still in development and in a discussion phase. In this contribution, POD results using an Artificial Intelligence (AI)-based data analysis are compared with those delivered by conventional data analysis. Using an open-access dataset from Open Guided Wave platform, the possibility of performance assessment for GW-based SHM systems using AI-based data analysis is shown in detail. An artificial neural network (ANN) classifier is trained to detect artificial damage in a stiffened CFRP plate. As input for the ANN, classical damage indicators are used. The ANN is tested to detect damage at another position, whose inspection data were not previously used in training. The findings show very high detection capabilities without sorting any specific path but only having a global view of current damage metrics. The systematic evaluation of the ANN predictions with respect to specific damage sizes allows to compute a probability of correct identification versus flaw dimension, somehow equivalent to and compared with the results achieved through classic path-based POD analysis. Also, sensitive paths are detected by ANN predictions allowing for evaluation of maximal distances between path and damage position. Finally, it is shown that the prediction performance of the ANN can be improved significantly by combining different damage indicators as inputs.
Issues that prevent Structural Health Monitoring (SHM) based on Guided Waves (GW) from being a part of today’s monitoring solutions in industry are not all obvious to the scientific community. To uncover and overcome these issues, scientists working on SHM and GW problems joined in an expert committee under the patronage of the German Society for Non-Destructive Testing. An initiated online survey among more than 700 experts and users reveals the hurdles hindering the practical application of GWbased SHM. Firstly, methods for proof of reliability of SHM approaches are missing.
Secondly, detailed understanding of phenomenological described wave-damage interactions is needed. Additionally, there are significant unsolved implementation issues and unsolved problems of signal processing including handling of environmental influences.
To enable substantial proof of reliability without unaffordable experimental effort also efficient simulation tools including realistic damage interaction are needed, enabling the joint use of experimental and simulated data to predict the capabilities of the Monitoring system. Considering these issues, the committee focusses on simulation, signal processing, as well as probability of detection and standardization. In the presented work, recent activities of the expert committee starting with survey results are summarized. An open access data basis of life-like measurements is presented to allow testing and comparison of signal processing and simulation algorithms. Finally, a strategy for efficient proof of reliability increasing the acceptance of SHM in industry and for successful Integration of SHM into real-world engineering structures is proposed.
Issues that prevent Structural Health Monitoring (SHM) based on Guided Waves (GW) from being a part of today’s monitoring solutions in industry are not all obvious to the scientific community. To uncover and overcome these issues, scientists working on SHM and GW problems joined in an expert committee under the patronage of the German Society for Non-Destructive Testing. An initiated online survey among more than 700 experts and users reveals the hurdles hindering the practical application of GWbased SHM. Firstly, methods for proof of reliability of SHM approaches are missing.
Secondly, detailed understanding of phenomenological described wave-damage interactions is needed. Additionally, there are significant unsolved implementation issues and unsolved problems of signal processing including handling of environmental influences.
To enable substantial proof of reliability without unaffordable experimental effort also efficient simulation tools including realistic damage interaction are needed, enabling the joint use of experimental and simulated data to predict the capabilities of the Monitoring system. Considering these issues, the committee focusses on simulation, signal processing, as well as probability of detection and standardization. In the presented work, recent activities of the expert committee starting with survey results are summarized. An open access data basis of life-like measurements is presented to allow testing and comparison of signal processing and simulation algorithms. Finally, a strategy for efficient proof of reliability increasing the acceptance of SHM in industry and for successful Integration of SHM into real-world engineering structures is proposed.
The reliability of data, obtained from nondestructive testing depends on many different factors as e.g. the equipment, the human factor, the influence of material properties, geometrical accessibility of the given detail, the structure of the detail, but also from data acquisition, processing and interpretation. The presentation introduces to current possibilities for the quantification of measurement results received during nondestructive testing.