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Series: ASM Handbook
Volume: 17
Publisher: ASM International
Published: 01 August 2018
DOI: 10.31399/asm.hb.v17.a0006463
EISBN: 978-1-62708-190-0
...Abstract Abstract Probability of detection (POD) assesses the performance of a non-destructive evaluation (NDE)-based inspection, which is a method used to determine the capability of an inspection as a function of defect type and defect size. This article provides an overview of the concept...
Abstract
Probability of detection (POD) assesses the performance of a non-destructive evaluation (NDE)-based inspection, which is a method used to determine the capability of an inspection as a function of defect type and defect size. This article provides an overview of the concept of POD, why it is needed, the history behind the development of POD, how POD assessments are performed, and how modeling and simulation can be integrated into the execution of a POD assessment. It describes the methods by which POD is determined. This includes detail on the experimental process to acquire the needed data, the mathematical methods to obtain a POD curve, and techniques to assess uncertainty in the POD curve as it is obtained from a limited data set. The concept of model-assisted POD (MAPOD) is introduced, with additional details and representative examples of MAPOD.
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in Reliability of Flaw Detection by Nondestructive Inspection
> Nondestructive Evaluation of Materials
Published: 01 August 2018
Fig. 26 Sample plot of probability of detection/probability of false alarm curves for different flaw sizes. Source: Ref 16
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Published: 01 August 2018
Fig. 7 Concepts of probability of detection (POD) and probability of false alarms (PFA) are determined by fractions of signal and noise distribution above a desired threshold. Signals generally increase as the flaw size increases, leading to a sigmoidal shape POD curve. Source: Ref 28
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Published: 01 August 2018
Fig. 1 Probability of detection (POD) concepts and the probability of false alarm (PFA) are determined by fractions of signal and noise distributions above a threshold. Signal distribution generally shifts to higher levels as flaw size increases, leading to the sigmoidal POD curve.
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Published: 01 January 2002
Fig. 4 Probability of detection concepts (POD) and probability of false alarm (PFA) are determined by fractions of signal and noise distributions above a threshold. Signal distribution generally shifts to higher levels as flaw size increases, leading to the sigmoidal POD curve. Source: Ref 22
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Published: 01 August 2018
Fig. 3 Illustration of a probabilistic concept of probability of detection (POD). At any crack size, different cracks of the same size will have varying detectability.
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in Reliability of Flaw Detection by Nondestructive Inspection
> Nondestructive Evaluation of Materials
Published: 01 August 2018
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in Reliability of Flaw Detection by Nondestructive Inspection
> Nondestructive Evaluation of Materials
Published: 01 August 2018
Fig. 19 Diagram of probability of detection (POD) model and its application to nondestructive evaluation (NDE) system qualification and optimization and to computer-aided design for inspectability
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in Reliability of Flaw Detection by Nondestructive Inspection
> Nondestructive Evaluation of Materials
Published: 01 August 2018
Fig. 22 Predicted influence of scan plan on probability of detection (POD). (a) POD at three depths for axial and circumferential scan increments of 2.5 mm (0.1 in.). (b) POD at three depths for axial and circumferential scan increments of 5 and 1.3 mm (0.2 and 0.05 in.), respectively
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Published: 01 August 2018
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Published: 01 January 2002
Fig. 9 Role of NDE, as quantified by probability of detection (POD), in fully probabilistic life management
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Published: 01 August 2018
Fig. 5 Model-assisted probability of detection (MAPOD) model-building process with complete approach to uncertainty propagation in MAPOD, from MIL-HDBK-1823A, Appendix H (2009). Source: Ref 31
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Published: 01 August 2018
Fig. 8 Mean probability of detection (POD) and 95% confidence for the raised head fastener probability of detection study. These are the aggregate of all results. Fits were calculated using the MIL-HDBK-1823 software
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Published: 01 August 2018
Fig. 10 Probability of detection curves by sample set quadrant for the transfer function. Source: Ref 52
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in Reliability of Flaw Detection by Nondestructive Inspection
> Nondestructive Evaluation of Materials
Published: 01 August 2018
Fig. 6 Probability of detection plots for four different nondestructive evaluation methods on the same set of specimens. (a) Penetrant inspection. (b) Ultrasonic inspection. (c) Eddy current inspection. (d) X-ray inspection
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in Reliability of Flaw Detection by Nondestructive Inspection
> Nondestructive Evaluation of Materials
Published: 01 August 2018
Fig. 14 Probability of detection curve for an automated fluorescent penetrant inspection system
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in Reliability of Flaw Detection by Nondestructive Inspection
> Nondestructive Evaluation of Materials
Published: 01 August 2018
Fig. 17 Retirement-for-cause system probability of detection curves for various geometrical features
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