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Proceedings Paper

Extraction of qualitative features from sensor data using windowed Fourier transform
Author(s): Abolfazl M. Amini; Fernando Figueroa
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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). Then each point of the signal is selected 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. The results indicate distinct shapes corresponding to each process.

Paper Details

Date Published: 9 August 2004
PDF: 9 pages
Proc. SPIE 5429, Signal Processing, Sensor Fusion, and Target Recognition XIII, (9 August 2004); doi: 10.1117/12.540334
Show Author Affiliations
Abolfazl M. Amini, Southern Univ. (United States)
Fernando Figueroa, NASA Stennis Space Ctr. (United States)


Published in SPIE Proceedings Vol. 5429:
Signal Processing, Sensor Fusion, and Target Recognition XIII
Ivan Kadar, Editor(s)

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