Location

La Jolla, CA

Start Date

1-1-1983 12:00 AM

Description

The objective of the work described in this paper is to develop signal analysis techniques that can automatically discriminate be-tween non-critical acoustic emission (AE) from crack growth and acoustic noise signals, such as fretting, of fasteners. The ultimate application of this work is for in-flight AE monitoring of critical aircraft structures.

Fatigue crack growth experiments were performed with center notched plate specimens and simulated joint specimens of 7075-T651 aluminum. The experimental conditions were controlled such that acoustic signals were obtained from crack growth, crack interface rubbing, and from fastener fretting.

This paper reports the results of pattern recognition analysis of the signals using autocorrelation lags and statistical measures of the signals and their power spectra as features. The goal of the pattern recognition analysis was to isolate crack growth AE signals from the other acoustic data. The results indicate that autocorrelation lags are the most important features for discriminating these signals.

Book Title

Review of Progress in Quantitative Nondestructive Evaluation

Volume

2A

Chapter

Section 9: Acoustic Emission

Pages

489-501

DOI

10.1007/978-1-4613-3706-5_29

Language

en

File Format

application/pdf

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Jan 1st, 12:00 AM

Pattern Recognition Analysis of Acoustic Emission Data from 7075-T651 Aluminum Simulated Joint Specimens

La Jolla, CA

The objective of the work described in this paper is to develop signal analysis techniques that can automatically discriminate be-tween non-critical acoustic emission (AE) from crack growth and acoustic noise signals, such as fretting, of fasteners. The ultimate application of this work is for in-flight AE monitoring of critical aircraft structures.

Fatigue crack growth experiments were performed with center notched plate specimens and simulated joint specimens of 7075-T651 aluminum. The experimental conditions were controlled such that acoustic signals were obtained from crack growth, crack interface rubbing, and from fastener fretting.

This paper reports the results of pattern recognition analysis of the signals using autocorrelation lags and statistical measures of the signals and their power spectra as features. The goal of the pattern recognition analysis was to isolate crack growth AE signals from the other acoustic data. The results indicate that autocorrelation lags are the most important features for discriminating these signals.