Probability of detection models for eddy current NDE methods

A considerable amount of attention has been focused in recent years towards the development of probability of detection (POD) models for a variety of nondestructive evaluation (NDE) methods. Interest in these models is motivated by a desire to quantify the variability introduced during the process of testing. As an example, sources of variability involved in eddy current methods of NDE include those caused by variations in liftoff, material properties, probe canting angle, scan format, surface roughness and measurement noise. Numerical models have been extensively used to model physical processes underlying NDE phenomena. Such models have been used, for example, to predict the transducer response for a given specimen geometry, defect configuration and test conditions. These models, however, are deterministic in nature and do not take into account variabilities associated with the inspection carried out in the field. This has limited the utility of deterministic models to practitioners in general since a considerable difference can exist between the nominal value of the transducer response predicted by the model and the actual measurement. This thesis presents a comprehensive POD model for eddy current NDE. Eddy current methods of nondestructive testing are used widely in industry to inspect a variety of nonferromagnetic and ferromagnetic materials. The development of a comprehensive POD model is therefore of significant importance. The model incorporates several sources of variability characterized by a multivariate Gaussian distribution and employs finite element analysis to predict the signal distribution. The method of mixtures is then used for estimating optimal threshold values. The research demonstrates the use of a finite element model within a probabilistic framework to predict the spread in the measured signal for eddy current nondestructive methods. Using the signal distributions for various flaw sizes the POD curves for varying defect parameters have been computed. In contrast to experimental POD models, the cost of generating such curves is very low and complex defect shapes can be handled very easily. The results are also operator independent

Nondestructive Testing Methods and New Applications

Nondestructive testing enables scientists and engineers to evaluate the integrity of their structures and the properties of their materials or components non-intrusively, and in some instances in real-time fashion. Applying the Nondestructive techniques and modalities offers valuable savings and guarantees the quality of engineered systems and products. This technology can be employed through different modalities that include contact methods such as ultrasonic, eddy current, magnetic particles, and liquid penetrant, in addition to contact-less methods such as in thermography, radiography, and shearography. This book seeks to introduce some of the Nondestructive testing methods from its theoretical fundamentals to its specific applications. Additionally, the text contains several novel implementations of such techniques in different fields, including the assessment of civil structures (concrete) to its application in medicine

New methods for statistical modeling and analysis of nondestructive evaluation data

Statistical methods have a long history of applications in physical sciences and engineering for design of experiments and data analyses. In nondestructive evaluation (NDE) studies, standard statistical methods are described in Military Handbook 1823A as guidelines to analyze the experimental NDE data both in carefully controlled laboratory setup and field studies. However complicated data structures often demand non-traditional statistical approaches. In this dissertation, with the inspiration and needs from actual NDE data applications, we introduced several statistical methods for better description of the problem and more appropriate modeling of the data. We also discussed the potential applications of those statistical methods to other research areas. The dissertation is organized as following. First a brief background introduction and overview are presented at Chapter 1. Then the complementary risk noise-interference model is discussed in Chapter 2 to better describe the noise and signal relation. In Chapter 3, a direct application of the noise interference model to vibrothermography NDE experiment scalar data is presented. In Chapter 4, the matched filter technique is used to increase signal-to-noise ratio for sequence of image analysis. In Chapter 5, the physical model assisted probability of detection analyses are introduced where the underlying physical mechanism plays an important role in the data interpretation. In Chapter 6, a bivariate normal Bayesian approach is studied to efficiently handle missing information. Finally we summarize these recent NDE developments at Chapter 7

NDE data fusion using phenomenological approaches

Data fusion techniques are beginning to attract considerable attention. In the NDE context, such techniques can be used to combine information from two or more NDE test methods to improve the probability of detection and enhance characterization results. An example of such an application involves the fusion of eddy current and ultrasonic NDE data. The eddy current phenomena relies on the diffusion process to propagate energy. Ultrasonic phenomena, in contrast, rely on wave propagation. The manner in which the energy interacts with the material under test is fundamentally different. It can therefore be argued that each test method provides a different perspective and consequently approaches that allow data from the two test methodologies to be fused have the potential for offering an improved understanding of the condition of the material;This dissertation presents an incremental step towards the development of a very novel phenomenological approach to data fusion. The method involves mapping of the wave field to a diffusion field using Q-transforms. The transformed and diffusion fields are then combined to synthesize the fused image. A systematic study of the issues involved in fusion and the registration of the data was conducted. The study was accomplished by developing and using a two-dimensional analytical model that includes both the diffusion and wave propagation contributions. The ultrasonic tests were simulated using an existing finite element model. The dissertation presents results obtained by transforming the ultrasonic data into the diffusion domain. The effect of Q-transform properties, especially its time shift property, on data registration is analyzed. A modified version of the Q-transform is also presented to overcome the problems associated with large differences in the values of the underlying partial differential equation coefficients. Theoretical results obtained using the approach together with a discussion on additional work that needs to be undertaken are presented

A prior knowledge based optimal Wiener filtering approach to ultrasonic scattering amplitude estimation

Advances in component life prediction techniques have prompted increased interest in quantitative nondestructive characterization of flaws in engineering materials. Flaw characterization techniques utilize a signature from the fLaw; In ultrasonics, the signature is estimated from noise-corrupted experimental measurements of the scattered acoustic wave field resulting from insonification of the fLaw; Estimating the flaw\u27s signature involves removing the effects of the measurement system in the presence of noise. In the frequency domain, the flaw\u27s signature is called a scattering amplitude. The purpose of this work is to evaluate an optimal Wiener filtering approach to scattering amplitude estimation;The scattering amplitude estimation problem is described with stochastic models in which noise and scattering amplitude are assumed to be random variables. The optimal Wiener filter is derived for the general case where scattering amplitude and noise are assumed to be uncorrelated, Gaussian random variables with known mean and variance. This derivation yields the important result that the filter determines an optimal estimate as the weighted average of the information derived from measurement of the scattered acoustic field and prior information about the flaw distribution. Experimental procedures for measuring flaw signals and for measuring acoustic noise are stated. Noise and scattering amplitude are analyzed as random variables with emphasis on evaluation of the assumptions made in deriving the filter. It is shown that acoustic noise has zero mean and is reasonably uncorrelated and Gaussian. Scattering amplitude is shown to be correlated and non-Gaussian for a lognormal distribution of volumetric scatterers. A novel approach is used to create noise-corrupted flaw signals which utilizes either measured or simulated flaw signals along with measured acoustic noise. The performance of the optimal Wiener filter in determining scattering amplitude estimates from noise-corrupted flaw signals is evaluated and compared to the presently employed on-optimal form of the Wiener filter. The optimal Wiener filter is shown to provide improved scattering amplitude estimates by filtering out acoustic noise and by utilizing prior flaw information;ftn;[superscript]1DOE Report IS-T 1257. This work was performed under contract No. W-7405-Eng-82 with the U.S. Department of Energy