Nonparametric Pod Estimation for Hit∕miss Data: A Goodness of Fit Comparison for Parametric Models

Spencer, Floyd W.

doi:10.1063/1.3592115

Cited by 6 publications

(6 citation statements)

References 3 publications

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“…Recently, concerns have been raised about the current approach, and binomial methods have once again been proposed to mitigate concerns about the behavior of POD for flaw sizes greater than the established a 90 estimates generated by parametric approaches (Generazio, 2011). Further work on nonparametric methods has been done by Spencer with the assumption that the POD is a continuous non-decreasing function of flaw size (Spencer, 2011).…”

Section: Current Methods Of Analysis For Hit/miss Datamentioning

confidence: 99%

Statistical Analysis of Hit/Miss Data (Preprint)

Knopp¹,

Zeng²

2012

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Section: Current Methods Of Analysis For Hit/miss Datamentioning

confidence: 99%

Statistical Analysis of Hit/Miss Data (Preprint)

Knopp¹,

Zeng²

2012

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“…Moreover, they can be used for other purposes such as sensitivity analysis evaluating the Sobol indices or to derive nonparametric POD curves. 160,163…”

Section: Multivariate-probability Of Detectionmentioning

confidence: 99%

“…Moreover, they can be used for other purposes such as sensitivity analysis evaluating the Sobol indices or to derive nonparametric POD curves. 160,163 Miorelli et al show that in the CIVA software metamodels can be derived using the Output Space Filling Criterion or the Support Vector Regression algorithm. This kind of solution is particularly important in MAPOD studies.…”

Section: Metamodelsmentioning

confidence: 99%

Probability of detection, localization, and sizing: The evolution of reliability metrics in Structural Health Monitoring

Falcetelli

Yue

Sante

et al. 2021

Structural Health Monitoring

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The successful implementation of Structural Health Monitoring (SHM) systems is confined to the capability of evaluating their performance, reliability, and durability. Although there are many SHM techniques capable of detecting, locating and quantifying damage in several types of structures, their certification process is still limited. Despite the effort of academia and industry in defining methodologies for the performance assessment of such systems in recent years, many challenges remain to be solved. Methodologies used in Non-Destructive Evaluation (NDE) have been taken as a starting point to develop the required metrics for SHM, such as Probability of Detection (POD) curves. However, the transposition of such methodologies to SHM is anything but straightforward because additional factors should be considered. The time dependency of the data, the larger amount of variability sources and the complexity of the structures to be monitored exacerbate/aggravate the existing challenges, suggesting that much work has still to be done in SHM. The article focuses on the current challenges and barriers preventing the development of proper reliability metrics for SHM, analyzing the main differences with respect to POD methodologies for NDE. It was found that the development of POD curves for SHM systems requires a higher level of statistical expertise and their use in the literature is still limited to few studies. Finally, the discussion extends beyond POD curves towards new metrics such as Probability of Localization (POL) and Probability of Sizing (POS) curves, reflecting the diagnosis paradigm of SHM.

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“…Other regularization processes are possible such as piecewise model or non-parametric POD estimation. 56,57 In Figure 7, the log ( a ^ ) versus log a data obtained (a) numerically and (b) experimentally are represented. Linear regression curve calculated over the data is also represented in both cases in Figure 7.…”

Section: Application Of the Model-based Assessment Approachmentioning

confidence: 99%

“…A linear relationship is obtained by taking the logarithm ofâ and of a values. Other regularization process are possible such as Piecewise model or nonparametric POD estimation [56,57]. In Fig.…”

Section: Detection and Localization Assessmentmentioning

confidence: 99%

A model-based approach for statistical assessment of detection and localization performance of guided wave–based imaging techniques

Moriot

Quaegebeur

Duff

et al. 2017

Structural Health Monitoring

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This paper aims at providing a framework for assessing the detection and localization performance of guided wave-based structural health monitoring (SHM) imaging systems. The assessment exploits a damage identification metric (DIM) providing a diagnostic of the structure from an image of the scatterers generated by the system, allowing detection, localization, and size estimation of the damage. Statistical probability of detection (POD) and probability of localization (POL) curves are produced based on values of the DIM for several damage sizes and positions. Instead of relying on arduous measurements on a significant number of structures instrumented in the same way, a model-based approach is considered in this paper for estimating POD and POL curves numerically. This approach is first illustrated on a simplistic model, which allows characterizing the robustness of the SHM system for various levels of noise in test signals. An experimental test case using a more realistic case with an artificial damage is then considered for validating the approach. A good agreement between experimental and numerical values of the DIM and derived POD and POL curves is observed.

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Nonparametric Pod Estimation for Hit∕miss Data: A Goodness of Fit Comparison for Parametric Models

Cited by 6 publications

References 3 publications

Statistical Analysis of Hit/Miss Data (Preprint)

Statistical Analysis of Hit/Miss Data (Preprint)

Probability of detection, localization, and sizing: The evolution of reliability metrics in Structural Health Monitoring

A model-based approach for statistical assessment of detection and localization performance of guided wave–based imaging techniques

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