Filtern
Erscheinungsjahr
Dokumenttyp
Sprache
- Englisch (12)
Referierte Publikation
- ja (12) (entfernen)
Schlagworte
- Inspection (12) (entfernen)
Organisationseinheit der BAM
In this work, we present a novel approach to photothermal super resolution based thermographic resolution of internal defects using two-dimensional pixel pattern-based active photothermal laser heating in conjunction with subsequent numerical reconstruction to achieve a high-resolution reconstruction of internal defect structures. With the proposed adoption of pixelated patterns generated using laser coupled high-power DLP projector technology the complexity for achieving true two-dimensional super resolution can be dramatically reduced taking a crucial step forward towards widespread practical viability. Furthermore, based on the latest developments in high-power DLP projectors, we present their first application for structured pulsed thermographic inspection of macroscopic metal samples. In addition, a forward solution to the underlying inverse problem is proposed along with an appropriate heuristic to find the regularization parameters necessary for the numerical inversion in a laboratory setting. This allows the generation of synthetic measurement data, opening the door for the application of machine learning based methods for future improvements towards full automation of the method. Finally, the proposed method is experimentally validated and shown to outperform several established conventional thermographic testing techniques while conservatively improving the required measurement times by a factor of 8 compared to currently available photothermal super resolution techniques.
Thermographic photothermal super resolution reconstruction enables the resolution of internal defects/inhomogeneities below the classical limit, which is governed by the diffusion properties of thermal wave propagation. Based on a combination of the application of special sampling strategies and a subsequent numerical optimization step in post-processing, thermographic super resolution has already proven to be superior to standard thermographic methods in the detection of one-dimensional defect/inhomogeneity structures. In our work, we report an extension of the capabilities of the method for efficient detection and resolution of defect cross sections with fully two-dimensional structured laser-based heating. The reconstruction is carried out using one of two different algorithms that are proposed within this work. Both algorithms utilize the combination of several coherent measurements using convex optimization and exploit the sparse nature of defects/inhomogeneities as is typical for most nondestructive testing scenarios. Finally, the performance of each algorithm is rated on reconstruction quality and algorithmic complexity. The presented experimental approach is based on repeated spatially structured heating by a high power laser. As a result, a two-dimensional sparse defect/inhomogeneity map can be obtained. In addition, the obtained results are compared with those of conventional thermographic inspection methods that make use of homogeneous illumination. Due to the sparse nature of the reconstructed defect/inhomogeneity map, this comparison is performed qualitatively.
Active thermography as a nondestructive testing modality suffers greatly from the limitations imposed by the diffusive nature of heat conduction in solids. As a rule of thumb, the detection and resolution of internal defects/inhomogeneities is limited to a defect depth to defect size ratio greater than or equal to one. Earlier, we demonstrated that this classical limit can be overcome for 1D and 2D defect geometries by using photothermal laser-scanning super resolution. In this work we report a new experimental approach using 2D spatially structured illumination patterns in conjunction with compressed sensing and computational imaging methods to significantly decrease the experimental complexity and make the method viable for investigating larger regions of interest.
Thermographic super-resolution techniques allow the resolution of defects/inhomogeneities beyond the classical limit, which is governed by the diffusion properties of thermal wave propagation. Photothermal super-resolution is based on a combination of an experimental scanning strategy and a numerical optimization which has been proven to be superior to standard thermographic methods in the case of 1D linear defects. In this contribution, we report on the extension of this approach towards a full frame 2D photothermal super-resolution technique. The experimental approach is based on a repeated spatially structured heating using high power lasers. In a second post-processing step, several measurements are coherently combined using mathematical optimization and taking advantage of the (joint) sparsity of the defects in the sample. In our work we extend the possibilities of the method to efficiently detect and resolve defect cross sections with a fully 2D-structured blind illumination.
Reliability analysis of deteriorating structural systems requires the solution of time-variant reliability problems.
In the general case, both the capacity of and the loads on the structure vary with time. This analysis can be approached by approximation through a series of time-invariant reliability problems, which is a potentially effective strategy for cases where direct solutions of the time-variant reliability problem are challenging, e.g. for structural systems with many elements or arbitrary load processes. In this contribution, we thoroughly Review the formulation of the equivalent time-invariant reliability problems and extend this approximation to structures for which inspection and monitoring data is available. Thereafter, we present methods for efficiently evaluating the reliability over time. In particular, we propose the combination of sampling-based methods with a FORM (first-order reliability method) approximation of the series system reliability problem that arises in the computation of the lifetime reliability. The framework and algorithms are demonstrated on a set of numerical examples, which include the computation of the reliability conditional on inspection data.
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.
An efficient approach to reliability analysis of deteriorating structural systems is presented, which considers stochastic dependence among element deterioration. Information on a deteriorating structure obtained through inspection or monitoring is included in the reliability assessment through Bayesian updating of the system deterioration model. The updated system reliability is then obtained through coupling the updated deterioration model with a probabilistic structural model. The underlying high-dimensional structural reliability problems are solved using subset simulation, which is an efficient and robust sampling-based algorithm suitable for such analyses. The approach is demonstrated in two case studies considering a steel frame structure and a Daniels system subjected to high-cycle fatigue.
Inspection and maintenance of concrete bridges is a major cost factor in transportation infrastructure, and there is significant potential for using information gained during inspection to update predictive models of the performance and reliability of such structures. In this context, this paper presents an approach for assessing and updating the reliability of prestressed concrete bridges subjected to chloride-induced reinforcement corrosion. The system deterioration state is determined based on a Dynamic Bayesian Network (DBN) model that considers the spatial variability of the corrosion process. The overall system reliability is computed by means of a probabilistic structural model coupled with the deterioration model. Inspection data are included in the system reliability calculation through Bayesian updating on the basis of the DBN model. As proof of concept, a software prototype is developed to implement the method presented here. The software prototype is applied to a typical highway bridge and the influence of inspection information on the system deterioration state and the structural reliability is quantified taking into account the spatial correlation of the corrosion process. This work is a step towards developing a software tool that can be used by engineering practitioners to perform reliability assessments of ageing concrete bridges and update their reliability with inspection and monitoring data.
Trans-European corridors for transport of people and goods require strengthening of bridges to carry increased axle loads in terms of absolute axle loads and of their frequency. Most of the bridges along highways are reinforced or prestressed concrete bridges.
Since the late 1990s, CFRP laminates have been applied to box girder and T-beam bridges to increase the overall capacity. Although regular inspections are carried out, no special method was available to test the bond condition of CFRP laminates after being in Service for more than ten years. The German Center of Competence in Civil Engineering is aware of this need and funded a research project to investigate the bond condition by means of advanced non-destructive testing (NDT).
For almost 15 years, carbon fibre-reinforced polymer (CFRP) plates and sheets have been used for strengthening and repair of civil engineering structures. CFRP plates were applied to upgrade structures for change in use or increasing traffic load. CFRP laminates have also been used to repair deteriorated structures such as multi-storey parking or bridges. The execution quality and condition of the strengthening measure in terms of bond integrity were neither monitored nor tested systematically. Inspectors eventually applied simple hammer knocking during regular main inspection for subjective noise distinction as a measure for eventual insufficient bond. This paper presents the feasibility and the limitations of low-frequency transversal ultrasonic waves with dry-coupling point-contact sensors for inspection and defect detection between CFRP laminate and concrete surfaces. The German Centre of Competence in Civil Engineering (DIBt) has initiated a national research project [Helmerich et al. (2012). Condition analysis of bonded CFRP-applications on concrete structures by means of non-destructive testing [in German: Zustandsuntersuchung von CFK-Klebeverstärkungen an Betonbauteilen mittels zerstörungsfreier Prüfung (ZfP)]. Final, internal project report], carried out at the Federal Institute for Materials Research and Testing (BAM), Germany. The aim of the project was to present a potential non-destructive testing method for bond defect detection as a measure for the durability of the strengthening system more than 10 years after application.
Concepts for digital image evaluation are presented for Computed Radiography (CR) and Digital Detector Arrays (DDAs) used for weld inspection. The precise DDA calibration yields an extra ordinary increase of contrast sensitivity up to 10 times in relation to film radiography. Restrictions in spatial resolution caused by pixel size of the DDA are compensated by increased contrast sensitivity. First CR standards were published in 2005 to support the application of phosphor imaging plates in lieu of X-ray film, but they need already a revision based on experiences reported by many users. One of the key concepts is the usage of signal-to-noise (SNR) measurements as equivalent to the optical density of film and film system class. The contrast sensitivity, measured by IQI visibility, depends on three essential parameters: The basic spatial resolution (SRb) of the radiographic image, the achieved signal-to-noise ratio (SNR) and the specific contrast (µeff - effective attenuation coefficient). Knowing these 3 parameters for the given exposure condition, inspected material and monitor viewing condition permits the calculation of the just visible IQI element. Furthermore, this enables the optimization of exposure conditions. The new ISO/FDIS 17636-2 describes the practice for digital radiography with CR and DDAs. It considers the first time compensation principles, derived from the three essential parameters. The consequences are described.