Sensor performance monitoring using Fourier and wavelet transforms

Abolfazl M. Amini

doi:10.1117/12.818263

19 March 2009 Sensor performance monitoring using fourier and wavelet transforms

Abolfazl M. Amini

Proceedings Volume 7343, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII; 734307 (2009) https://doi.org/10.1117/12.818263
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

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.

Citation Download Citation

Abolfazl M. Amini "Sensor performance monitoring using fourier and wavelet transforms", Proc. SPIE 7343, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII, 734307 (19 March 2009); https://doi.org/10.1117/12.818263

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