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Proceedings Paper

Wavelet analysis of sensor data for qualitative features extraction
Author(s): Abolfazl Mahiari Amini
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Paper Abstract

The health of a sensor and system is monitored by information gathered from the sensor. A normal mode of operation is established. Any deviation from the normal behavior indicates a change. An RC network is used to model the main process, which is defined by a step-up (charging), drift, and step-down (discharging). The sensor disturbances and spike are added while the system is in drift. The system runs for a period of at least three time-constants of the main process every time a process feature occurs (e.g. step change). To extract time information and shape isolation the Wavelet Transform is used. The results are analyzed using continuous as well as discrete wavelet transforms. The results indicate distinct shapes corresponding to each process. The Wavelet Transform results are compared to the signal average power using hamming window and Fourier Transform. The Fourier Transform analysis of the signal is carried out by selecting each point of the signal with a window of trailing data collected previously. Two trailing window lengths are selected; one equal to two time constant of the main process and the other equal to two time constant of the sensor disturbance. Next, the DC is removed from each set of data and then the data are passed through a window followed by calculation of spectra for each set. In order to extract features, the signal power, peak, and spectral area are plotted vs. time

Paper Details

Date Published: 28 March 2005
PDF: 9 pages
Proc. SPIE 5818, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks III, (28 March 2005); doi: 10.1117/12.602580
Show Author Affiliations
Abolfazl Mahiari Amini, Southern Univ. (United States)

Published in SPIE Proceedings Vol. 5818:
Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks III
Harold H. Szu, Editor(s)

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