Symbolic time series analysis of ultrasonic data for early detection of fatigue damage

被引：72

作者：

Gupta, Shalabh ^{[1
]}

Ray, Asok ^{[1
]}

Keller, Eric ^{[1
]}

机构：

[1] Penn State Univ, University Pk, PA 16802 USA

来源：

MECHANICAL SYSTEMS AND SIGNAL PROCESSING | 2007年 / 21卷 / 02期

关键词：

symbolic time series analysis; pattern recognition; neural networks; anomaly detection;

D O I：

10.1016/j.ymssp.2005.08.022

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

This paper presents a novel analytical tool for early detection of fatigue damage in polycrystalline alloys that are commonly used in mechanical structures. Time series data of ultrasonic sensors have been used for anomaly detection in the statistical behaviour of structural materials, where the analysis is based on the principles of symbolic dynamics and automata theory. The performance of the proposed method has been evaluated relative to existing pattern recognition tools, such as neural networks and principal component analysis, for detection of small changes in the statistical characteristics of the observed data sequences. This concept is experimentally validated on a special-purpose test apparatus for 7075-T6 aluminium alloy specimens, where the anomalies accrue from small fatigue crack growth. (c) 2005 Elsevier Ltd. All rights reserved.

引用

页码：866 / 884

页数：19

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