Statistical Assessment of Probability of Detection for Automated Eddy Current Nondestructive Evaluation Inspection

Li, Ming; Nakagawa, N.; Larson, Brian; Meeker, William Q.

doi:10.1080/09349847.2012.735352

Cited by 5 publications

(6 citation statements)

References 7 publications

(5 reference statements)

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“…After β 0 , β 1 , and σ are obtained by a simple regression analyses, POD(a) is calculated as the probability thatâ is greater than a given threshold,â th , on the premise that four requirements, linearity of the parameters, uniform variance, uncorrelated observations, and normal errors [2], are fulfilled. Whereas recent studies have proposed the modifications of Equation (1) so that developed POD can evaluate the probability of false alarm [12] or the asymptotic property in actual inspections [13,14], Equation (1) accompanied by the four requirements, linearity of the parameters, uniform variance, uncorrelated observations, and normal errors, has been used as one of the most important backgrounds to construct POD on the basis of statistical analyses since its development in the 1980s. This study points out two issues concerning the conventional method stated above.…”

Section: Proposal Of a Pod Model 21 Conventional Methods To Construct Pod And Their Problemsmentioning

confidence: 99%

Evaluation of probability of detection (POD) studies with multiple explanatory variables

Yusa

Knopp

2015

Journal of Nuclear Science and Technology

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This study proposes a new method to evaluate a probability of detection (POD) as a function of more than one flaw parameter. The main idea of the method is to assume that signals due to flaws described with a given parameter x have a normal distribution of a mean of μ( x) and a standard deviation of σ ( x), and to use a combination of signals calculated by numerical simulations and experimental data to evaluate μ( x) and σ ( x), respectively. The method does not postulate a closed-form of μ( x) found in conventional approaches, and it evaluates a few parameters that characterize the distribution using maximum likelihood analysis to calculate POD. This allows POD evaluation for data that does not satisfy linearity or constant variance assumptions without transformation. The proposed method is demonstrated through analyzing simulated eddy current signals due to flaws appearing in type 316L stainless steel welds. The results of the demonstration confirm that the proposed method can provide the POD with its confidence bounds as a function of the depth and the length of a flaw. The results also showed that the proposed method does not require a large amount of experimental data compared to conventionalâ vs. a analysis.

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Section: Proposal Of a Pod Model 21 Conventional Methods To Construct Pod And Their Problemsmentioning

confidence: 99%

Evaluation of probability of detection (POD) studies with multiple explanatory variables

Yusa

Knopp

2015

Journal of Nuclear Science and Technology

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“…This mathematical description can be used for the POD evaluation instead of a linear function. A simplified model is presented in Equation (1) In other POD evaluations of eddy current testing, the size of the defect on the top of the surface, ie the crack size or the hole diameter, has been shown to influence the signal [4,10,11] . For holelike defects, large diameters cause a bigger disturbance to the eddy current field and thus a larger signal from the ET system.…”

Section: Development Of the Advanced Pod Approach For Low-frequency Ementioning

confidence: 99%

Probability of detection for surface-breaking holes with low-frequency eddy current testing – a non-linear multi-parametric approach

Kanzler¹,

Müller²,

Pitkänen³

2014

Insight

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The evaluation of non-destructive testing (NDT) methods in terms of reliability is an increasing demand in various industries and applications. The probability of detection (POD) is the most frequently used method for this task. In the testing of holes with low-frequency eddy current testing, the frequently-used one-parametric POD approach cannot be used because the requirements, mainly the linearity, cannot be met. Therefore, the use of a multi-parametric non-linear regression approach to calculate the POD is proposed. Instead of numerical simulations, commonly used in multiparametric approaches, an analytical model is used. The goal of this work is to evaluate the reliability of the eddy current system for the testing of electron-beam welded copper canisters by calculating a POD with the help of different artificial hole-like defects. In this example, the multi-parametric non-linear regression approach is shown to be successful, enabling the combination of depth and diameter in the POD calculation.

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“…Therefore we use a simulated data set for rivet holes used in lap-splices on aircraft bodies to illustrate our proposed inspection procedures and to present the joint estimation statistical methodology. The parameters used in the simulation are based on previous experience with eddy current NDE inspections for rivet holes, as described in Hovey, Meeker, and Li (2009) and Li, Nakagawa, Larson, and Meeker (2010).…”

Section: In-service Inspection Of Aircraft Lap-splice Rivet Holesmentioning

confidence: 99%

“…The actual eddy current NDE response is the maximum of the signal response and the noise response. That is, (6-4) Li and Meeker (2009) and Li, Nakagawa, Larson, and Meeker (2010) used the noise interference model to describe NDE measurement responses in other applications.…”

Section: Noise Interference Modelmentioning

confidence: 99%

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New methods for statistical modeling and analysis of nondestructive evaluation data

Li¹

Self Cite

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Meeker asking whether I was interested in his NDE projects as RA. After several long talks with Dr. Meeker, I decided to pursue a Statistics Ph.D. focusing on engineering applications. I came to realize the great opportunity of interdisciplinary research fields of Statistics, Physical Sciences and Engineering during these in-depth discussions. Step by step, Dr. Meeker lead me into the fantastic world of Statistics. Little by little, I learned how to apply my statistics and physics knowledge to real world problems. Dr. Meeker gave me enormous help and valuable advice not only in academic research but also in personal life. I have learned a lot from his deep understanding and sharp vision in statistics research fields. Here I would like to sincerely thank my advisor Dr. Meeker. Also during my Statistics Ph.D. study, I learnt a lot from my study committee: Dr. Holland explained the details of the NDE experimental setup and helped me to develop statistical models; Dr. Maitra suggested numerical and computational strategies for my model simulation; Dr. Nordman pointed potential applications of my research results; and Dr. Wu discussed the future development of statistics applications in engineering, economy and finance fields. I would like to thank my study committee for their insightful suggestion, numerous help and continuous encouragement. I would also like to thank Dr. Bruce Thompson and Dr. Peter Hovey for valuable discussions and help at the MAPOD project and BVN project. I would also like to thank all my friends during my eight years stay in Ames. Last I thank my wife and parents who always encourage and support my study.

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Statistical Assessment of Probability of Detection for Automated Eddy Current Nondestructive Evaluation Inspection

Cited by 5 publications

References 7 publications

Evaluation of probability of detection (POD) studies with multiple explanatory variables

Evaluation of probability of detection (POD) studies with multiple explanatory variables

Probability of detection for surface-breaking holes with low-frequency eddy current testing – a non-linear multi-parametric approach

New methods for statistical modeling and analysis of nondestructive evaluation data

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