The Influence of Calibration and Acceptance Criteria on Crack Detection and Discrimination by Eddy Current Techniques

Rummel, Ward D.; Christner, Brent K.; Mullen, Steven J.

doi:10.1007/978-1-4615-7763-8_101

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…The results, used in [6], are listed in Table 1. [3] and Martin Marietta [7], using the same air-core probe as in 1984 [1]. Figure 3 shows computed magnitude and phase profiles for rectangular and semi-elliptical fatigue crack models.…”

Section: Uniform Interrogating Fieldmentioning

confidence: 99%

Semi-Elliptical Surface Flaw EC Interaction and Inversion: Theory

Auld¹,

Jefferies²,

Moulder³

et al. 1986

Review of Progress in Quantitative Nondestructive Evaluation

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Section: Uniform Interrogating Fieldmentioning

confidence: 99%

Semi-Elliptical Surface Flaw EC Interaction and Inversion: Theory

Auld¹,

Jefferies²,

Moulder³

et al. 1986

Review of Progress in Quantitative Nondestructive Evaluation

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“…The 29/29 method 4 was among the first attempts to quantify the POD. It counts the number of damages detected n and divides it by the total number of inspected damages n tot to obtain an estimate for the POD = n / n tot .…”

Section: Introductionmentioning

confidence: 99%

Predictive probability of detection curves based on data from undamaged structures

Mendler,

Döhler,

Grosse

2023

Structural Health Monitoring

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This paper develops a model-assisted approach for determining predictive probability of detection curves. The approach is “model-assisted,” as the damage-sensitive features are evaluated in combination with a numerical model of the examined structure. It is “predictive” in the sense that probability of detection (POD) curves can be constructed based on measurement records from the undamaged structure, avoiding any destructive tests. The approach can be applied to a wide range of damage-sensitive features in structural health monitoring and non-destructive testing, provided the statistical distribution of the features can be approximated by a normal distribution. In particular, it is suitable for global vibration-based features, such as modal parameters, and evaluates changes in local structural components, for example, changes in material properties, cross-sectional values, prestressing forces, and support conditions. The approach explicitly considers the statistical uncertainties of the features due to measurement noise, unknown excitation, or other noise sources. Moreover, through confidence intervals, it considers model-based uncertainties due to uncertain structural parameters and a possible mismatch between the modeled and the real structure. Experimental studies based on a laboratory beam structure demonstrate that the approach can predict the POD before damage occurs. Ultimately, several ways to utilize predictive POD curves are discussed, for example, for the evaluation of the most suitable measurement equipment, for quality control, for feature selection, or sensor placement optimization.

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