Probability of Detection (PoD) Curves Based on Weibull Statistics

Ali, Mohamed Subair Syed Akbar; Rajagopal, Prabhu

doi:10.1007/s10921-018-0468-2

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…The probability of detection (or PoD) is a metric that is often used to quantify the defect detection capabilities of a given inspection approach. 34,35 Here, hit/miss-based PoD curves are considered for quantifying the performance of the proposed technique. A log-odd fit is used in hit/miss analysis.…”

Section: Introductionmentioning

confidence: 99%

Statistics-based baseline-free approach for rapid inspection of delamination in composite structures using ultrasonic guided waves

Jagadeeshwar

Kalyani

Rajagopal

et al. 2022

Structural Health Monitoring

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Delamination in composite structures is characterized by a resonant cavity wherein a fraction of an ultrasonic guided wave may be trapped. Based on this wave trapping phenomenon, we propose a baseline-free statistical approach for the identification and localization of delamination using sparse sampling and density-based spatial clustering of applications with noise (DBSCAN) technique. The proposed technique can be deployed for rapid inspection with minimal human intervention. The Performance of the proposed technique in terms of its ability to determine the precise location of such defects is quantified through the probability of detection measurements. The robustness of the proposed technique is tested through extensive simulations consisting of different random locations of defects on flat plate structures with different sizes and orientation as well as different values of signal to noise ratio of the simulated data. The simulation results are also validated using experimental data and the results are found to be in good agreement.

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Section: Introductionmentioning

confidence: 99%

Statistics-based baseline-free approach for rapid inspection of delamination in composite structures using ultrasonic guided waves

Jagadeeshwar

Kalyani

Rajagopal

et al. 2022

Structural Health Monitoring

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“…Improvements to the classical Berens [10] model have been proposed to behave better with very limited data sets, e.g. by Syed Akbar Ali et al [11], Syed Akbar Ali and Rajagopal [12] and Le Gratiet et al [13].…”

Section: Introductionmentioning

confidence: 99%

Comparison of â Versus a and Hit/Miss POD-Estimation Methods: A European Viewpoint

et al. 2019

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For estimating the probability of detection (POD) in non-destructive evaluation (NDE), there are two standard methods, the so-called â versus a approach and the hit/miss approach. The two approaches have different requirements for the quality and quantity of input data as well as for the underlying NDE method. There is considerable overlap between the methods, and they have different limitations, so it is of interest to study the differences arising from using each methodology. In particular, if the dataset is not ideal, the methodologies may exhibit different problems dealing with various limitations in the data. In this paper, a comparison between â versus a and hit/miss analysis was completed for two different data sets, a manual aerospace eddy-current inspection and a nuclear industry phased array ultrasonic weld inspection using a simplified online tool. It was found that the two standard methods (â vs. a and hit/miss) may give significantly different results, if the true hit/miss decision is based on inspector judgement and not automated signal threshold. The true inspector hit/miss performance shows significant variance that is not attributable to signal amplitude. Model-assisted POD was not able to model the inspector performance due to lack of representative amplitude threshold and difficulties in capturing true signal variance. The paper presents experience from practical cases and may be considered a European viewpoint.

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Outlier analysis for defect detection using sparse sampling in guided wave structural health monitoring

Jagadeeshwar

Kanakambaran

Kalyani

et al. 2021

Struct Control Health Monit

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Summary We propose an outlier detection‐based statistical approach to identify and locate a defect in composite plates using far fewer number of sensing points compared to conventional imaging techniques. The key steps involved in this computationally inexpensive approach are the random sparse selection of the sensing points through Poisson disk sampling, followed by a two‐step outlier detection process based on thresholding and computation of median absolute deviation. The robustness of the proposed technique is explored through extensive simulations involving different defect sizes, random locations on flat plate structures, and various values of signal to noise ratio (SNR). We experimentally demonstrate the feasibility of detection of delamination, whose size is comparable to the ultrasonic wavelength with probability of detection (PoD) better than 90% using <1% of the total number of samples required for conventional imaging, even under conditions wherein the SNR is as low as 5 dB.

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Probability of Detection (PoD) Curves Based on Weibull Statistics

Cited by 5 publications

References 16 publications

Statistics-based baseline-free approach for rapid inspection of delamination in composite structures using ultrasonic guided waves

Statistics-based baseline-free approach for rapid inspection of delamination in composite structures using ultrasonic guided waves

Comparison of â Versus a and Hit/Miss POD-Estimation Methods: A European Viewpoint

Outlier analysis for defect detection using sparse sampling in guided wave structural health monitoring

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