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Proceedings Paper

Sensor performance monitoring using Fourier and wavelet transforms
Author(s): Abolfazl M. Amini
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Paper Abstract

In this paper, we simulate sensor behavior by synthesizing different features of a sensor output under normal mode of operation. Any deviation from the normal behavior indicates a change. The synthesized features indicate behavior of different physical processes that a sensor is monitoring. The shapes of these features must be extracted from sensor output for sensor health management. The extracted features are compared to features of healthy sensor to monitor its performance. In this paper, we compare Fourier and Wavelet Transform methods for extraction of the sensor output features. The wavelet Transform Analysis is performed on the simulated data described above with Poisson distributed noise. The simulated data with Poisson distributed noise of SNRs ranging from 10 to 500 are generated. The data are analyzed using Discrete as well as Discretized Continuous Wavelet Transforms. The Short-Time Fourier Transform of the Signal is taken using the Hamming window. Three window widths are used. The DC value is removed from the windowed data prior to taking the FFT. The resulting three dimensional spectral plots provide good time frequency resolution. The results indicate distinct shapes corresponding to each process.

Paper Details

Date Published: 19 March 2009
PDF: 8 pages
Proc. SPIE 7343, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII, 734307 (19 March 2009); doi: 10.1117/12.818263
Show Author Affiliations
Abolfazl M. Amini, Southern Univ. (United States)

Published in SPIE Proceedings Vol. 7343:
Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII
Harold H. Szu; F. Jack Agee, Editor(s)

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