Location

Santa Cruz, CA

Start Date

1-1-1984 12:00 AM

Description

The detection of the presence of flaws in structural materials is the most important function which Non-Destructive Evaluation (NDE) performs. As structures are designed to higher performance criteria and as safety and life cycle cost factors become more important, it becomes necessary to detect smaller and more difficult to find flaws. This paper presents a practical approach to the optimum detection of flaws in the presence of noise signals. A decision theoretic approach (described in more detail in a companion paper by Fertig, et al.1) is used to derive a detection algorithm which is adapted to the noise environment in which a particular measurement is being made. An automatic procedure for characterizing the noises and developing the optimum detection algorithm is presented. Two implementations of this approach have been tested on experimental data and show substantial improvement over conventional detection techniques. One is a flexible algorithm used for research purposes, and the other is a real-time algorithm suitable for field implementation.

Book Title

Review of Progress in Quantitative Nondestructive Evaluation

Volume

3A

Chapter

Chapter 2: Ultrasonics

Section

Probability of Detection

Pages

81-93

DOI

10.1007/978-1-4684-1194-2_8

Language

en

File Format

application/pdf

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Jan 1st, 12:00 AM

Statistical Flaw Detection: Application to Flaws Below Curved Surfaces

Santa Cruz, CA

The detection of the presence of flaws in structural materials is the most important function which Non-Destructive Evaluation (NDE) performs. As structures are designed to higher performance criteria and as safety and life cycle cost factors become more important, it becomes necessary to detect smaller and more difficult to find flaws. This paper presents a practical approach to the optimum detection of flaws in the presence of noise signals. A decision theoretic approach (described in more detail in a companion paper by Fertig, et al.1) is used to derive a detection algorithm which is adapted to the noise environment in which a particular measurement is being made. An automatic procedure for characterizing the noises and developing the optimum detection algorithm is presented. Two implementations of this approach have been tested on experimental data and show substantial improvement over conventional detection techniques. One is a flexible algorithm used for research purposes, and the other is a real-time algorithm suitable for field implementation.