Title :
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
fDate :
June 29 2011-July 1 2011
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;
Conference_Titel :
American Control Conference (ACC), 2011
Conference_Location :
San Francisco, CA
Print_ISBN :
978-1-4577-0080-4
DOI :
10.1109/ACC.2011.5991263
