Tags: Transform, Fourier, Shorttime, using, Data, Sensor, from, Extraction, Features, Qualitative, Qualitative Features Extraction from Sensor Data using Shorttime Fourier Transform,
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Qualitative Features Extraction from Sensor Data using Short-time Fourier Transform
#1

Qualitative Features Extraction from Sensor Data using Short-time Fourier Transform

ABSTRACT

The information gathered from sensors is used to determine the health of a sensor. Once a normal mode of operation is
established any deviation from the normal behavior indicates a change. This change may be due to a malfunction of the
sensor(s) or the system (or process). The step-up and step-down features, as well as sensor disturbances are assumed to be
exponential. 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). 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.
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#2

The information collected from the sensors is used to determine the health of a sensor. Once a normal mode of operation is established, any deviation from normal behavior indicates a change. This change may be due to a malfunction of the sensor (s) or system (or process). It is assumed that the stepped and decreasing characteristics, as well as the sensor disturbances, are exponential. An RC network is used to model the main process, which is defined by a step-up, drift, and step-down. Sensor and peak disturbances are added while the system is drifting. The system runs for a period of at least three main process time constants each time a process characteristic (eg, step change) occurs. The Short-Time Signal Fourier Transform is taken with the Hamming window. Three window widths are used. The DC value is removed from the data in windows before taking the FFT.
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