Optimal partitioning of ultrasonic data for fatigue damage detection?

Author

Singh, D.S. ; Sarkar, S. ; Gupta, S. ; Ray, A.

Author_Institution

Dept. of Mech. Eng., Pennsylvania State Univ., University Park, PA, USA

fYear

2011

fDate

June 29 2011-July 1 2011

Firstpage

798

Lastpage

803

Abstract

This paper presents an analytical tool for online fatigue damage detection in polycrystalline alloys that are commonly used in mechanical structures. The underlying theory is built upon symbolic dynamic filtering (SDF) that optimally partitions time series data for feature extraction and pattern classification. The proposed method has been experimentally validated on a fatigue test apparatus that is equipped with ultrasonics sensors and a traveling optical microscope for fatigue damage detection.

Keywords

acoustic signal processing; fatigue; fatigue testing; feature extraction; filtering theory; pattern classification; sensors; structural engineering; ultrasonic applications; SDF; fatigue test apparatus; feature extraction; mechanical structures; online fatigue damage detection; optimal partitioning; pattern classification; polycrystalline alloys; symbolic dynamic filtering; time series data; traveling optical microscope; ultrasonic data; ultrasonic sensors; underlying theory; Acoustics; Fatigue; Feature extraction; Optimization; Sensors; Time series analysis; Training; Damage Classification; Fatigue Crack Initiation; Optimal feature extraction; Pattern Identification; Symbolic Dynamics;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference (ACC), 2011

Conference_Location

San Francisco, CA

ISSN

0743-1619

Print_ISBN

978-1-4577-0080-4

Type

conf

DOI

10.1109/ACC.2011.5991263

Filename

5991263