Master of Science in Electrical Engineering (MSEE)
Electrical and Computer Engineering
This work studies a fault detection method which analyzes sensor data for changes in their characteristics to detect the occurrence of faults in a dynamic system. The test system considered in this research is a Boeing-747 aircraft system and the faults considered are the actuator faults in the aircraft. The method is an alternative to conventional fault detection method and does not rely on analytical mathematical models but acquires knowledge about the system through experiments. In this work, we test the concept that the energy distribution of resolution than the windowed Fourier transform. Verification of the proposed methodology is carried in two parts. The first set of experiments considers entire data as a single window. Results show that the method effectively classifies the indicators by more that 85% as correct detections. The second set of experiments verifies the method for online fault detection. It is observed that the mean detection delay was less than 8 seconds. We also developed a simple graphical user interface to run the online fault detection.
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Vongala, Venkata S., "Knowledge-based fault detection using time-frequency analysis" (2005). LSU Master's Theses. 3716.
Jorge L. Aravena