Title :
Symbolic dynamics of wavelet images for pattern identification
Author :
Xin Jin ; Gupta, S. ; Mukherjee, K. ; Ray, A.
Author_Institution :
Dept. of Mech. Eng., Pennsylvania State Univ., University Park, PA, USA
fDate :
June 30 2010-July 2 2010
Abstract :
Symbolic time series analysis has been introduced in recent literature for pattern identification in dynamical systems. Relevant information, embedded in the measured time series, is extracted in the form of symbol sequences by partitioning of the data sets, and probabilistic finite state automata are constructed from these symbol sequences to generate pattern vectors. This paper presents a symbolic pattern identification method by partitioning of two-dimensional wavelet (i.e., scale-shift) images of sensor time series data. The proposed method is experimentally validated on a laboratory apparatus for identification of evolving patterns due to fatigue damage in polycrystalline alloy specimens.
Keywords :
feature extraction; finite state machines; image recognition; probabilistic automata; time series; wavelet transforms; data set partitioning; dynamical system identification; fatigue damage; feature extraction; pattern vectors; polycrystalline alloy specimens; probabilistic finite state automata; sensor time series data; symbol sequences; symbolic pattern identification method; symbolic time series analysis; two-dimensional wavelet image partitioning; wavelet image symbolic dynamics; Automata; Data mining; Feature extraction; Filtering; Neural networks; Principal component analysis; Signal analysis; Time series analysis; Wavelet analysis; Wavelet transforms; Anomaly detection; Feature extraction; Pattern Identification; Symbolic dynamics; Wavelet Images;
Conference_Titel :
American Control Conference (ACC), 2010
Conference_Location :
Baltimore, MD
Print_ISBN :
978-1-4244-7426-4
DOI :
10.1109/ACC.2010.5531077
