709 research outputs found

    Miniature mobile sensor platforms for condition monitoring of structures

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    In this paper, a wireless, multisensor inspection system for nondestructive evaluation (NDE) of materials is described. The sensor configuration enables two inspection modes-magnetic (flux leakage and eddy current) and noncontact ultrasound. Each is designed to function in a complementary manner, maximizing the potential for detection of both surface and internal defects. Particular emphasis is placed on the generic architecture of a novel, intelligent sensor platform, and its positioning on the structure under test. The sensor units are capable of wireless communication with a remote host computer, which controls manipulation and data interpretation. Results are presented in the form of automatic scans with different NDE sensors in a series of experiments on thin plate structures. To highlight the advantage of utilizing multiple inspection modalities, data fusion approaches are employed to combine data collected by complementary sensor systems. Fusion of data is shown to demonstrate the potential for improved inspection reliability

    High resolution ultrasonic spectroscopy system for nondestructive evaluation

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    With increased demand for high resolution ultrasonic evaluation, computer based systems or work stations become essential. The ultrasonic spectroscopy method of nondestructive evaluation (NDE) was used to develop a high resolution ultrasonic inspection system supported by modern signal processing, pattern recognition, and neural network technologies. The basic system which was completed consists of a 386/20 MHz PC (IBM AT compatible), a pulser/receiver, a digital oscilloscope with serial and parallel communications to the computer, an immersion tank with motor control of X-Y axis movement, and the supporting software package, IUNDE, for interactive ultrasonic evaluation. Although the hardware components are commercially available, the software development is entirely original. By integrating signal processing, pattern recognition, maximum entropy spectral analysis, and artificial neural network functions into the system, many NDE tasks can be performed. The high resolution graphics capability provides visualization of complex NDE problems. The phase 3 efforts involve intensive marketing of the software package and collaborative work with industrial sectors

    Markov Chain Monte Carlo Defect Identification in NDE Images

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    We derive a hierarchical Bayesian method for identifying elliptically‐shaped regions with elevated signal levels in NDE images. We adopt a simple elliptical parametric model for the shape of the defect region and assume that the defect signals within this region are random following a truncated Gaussian distribution. Our truncated‐Gaussian model ensures that the signals within the defect region are higher than the baseline level corresponding to the noise‐only case. We derive a closed‐form expression for the kernel of the posterior probability distribution of the location, shape, and defect‐signal distribution parameters (model parameters). This result is then used to develop Markov chain Monte Carlo (MCMC) algorithms for simulating from the posterior distributions of the model parameters and defect signals. Our MCMC algorithms are appliedsequentially to identify multiple potential defect regions. For each potential defect, we construct Bayesian confidence regions for the estimated parameters. Estimated Bayes factors are utilized to rank potential defects (discovered by our sequential scheme) according to goodness of fit. The performance of the proposed methods is demonstrated on experimental ultrasonic C‐scan data from an inspection of a cylindrical titanium billet

    Deep learning in automated ultrasonic NDE -- developments, axioms and opportunities

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    The analysis of ultrasonic NDE data has traditionally been addressed by a trained operator manually interpreting data with the support of rudimentary automation tools. Recently, many demonstrations of deep learning (DL) techniques that address individual NDE tasks (data pre-processing, defect detection, defect characterisation, and property measurement) have started to emerge in the research community. These methods have the potential to offer high flexibility, efficiency, and accuracy subject to the availability of sufficient training data. Moreover, they enable the automation of complex processes that span one or more NDE steps (e.g. detection, characterisation, and sizing). There is, however, a lack of consensus on the direction and requirements that these new methods should follow. These elements are critical to help achieve automation of ultrasonic NDE driven by artificial intelligence such that the research community, industry, and regulatory bodies embrace it. This paper reviews the state-of-the-art of autonomous ultrasonic NDE enabled by DL methodologies. The review is organised by the NDE tasks that are addressed by means of DL approaches. Key remaining challenges for each task are noted. Basic axiomatic principles for DL methods in NDE are identified based on the literature review, relevant international regulations, and current industrial needs. By placing DL methods in the context of general NDE automation levels, this paper aims to provide a roadmap for future research and development in the area.Comment: Accepted version to be published in NDT & E Internationa

    Uncertainty and sensitivity analysis of functional risk curves based on Gaussian processes

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    A functional risk curve gives the probability of an undesirable event as a function of the value of a critical parameter of a considered physical system. In several applicative situations, this curve is built using phenomenological numerical models which simulate complex physical phenomena. To avoid cpu-time expensive numerical models, we propose to use Gaussian process regression to build functional risk curves. An algorithm is given to provide confidence bounds due to this approximation. Two methods of global sensitivity analysis of the models' random input parameters on the functional risk curve are also studied. In particular, the PLI sensitivity indices allow to understand the effect of misjudgment on the input parameters' probability density functions

    ENIQ Technical Report - Link Between Risk-Informed In-Service Inspection and Inspection Qualification

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    There is a growing need for a quantitative measure of inspection effectiveness as an input to quantitative risk-informed in-service inspection (RI-ISI). A Probability of Detection (POD) curve could provide a suitable metric. However there can be significant problems associated with generating realistic POD curves by practical trials. The ENIQ inspection qualification methodology can provide high assurance that an inspection system will achieve its objectives, but is not designed to provide a quantitative measure of the type that can be used in RI-ISI analysis. A project, led by Doosan Babcock, was therefore set up with main objectives to investigate approaches to quantifying the confidence associated with inspection qualification and to produce guidelines on how to relate inspection qualification results, risk reduction and inspection interval. This report discusses how a simplified POD curve, such as a step curve, could be used as the target for inspection qualification, or as an output from it. Work to investigate the sensitivity of relative risk reduction to the details of the POD curve is described from which it is concluded that use of a simplified POD curve could be justified. Two methods for quantifying the outcome from inspection qualification are described. The first method is a relatively simple process based on direct expert judgement. The second method is based on a more rigorous structured process employing Bayesian statistics, in which the subjective degree of belief in inspection capability derived from a Technical Justification (TJ) is expressed in probabilistic terms, and combined with data from practical trials results. Two pilot studies are described which involved a qualification body applying the quantification methods in practice. Recommendations for further work to make the approaches developed more robust are provided.JRC.F.5-Safety of present nuclear reactor
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