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http://purl.umn.edu/119032
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| Title: | Robust, model-based fault detection for commercial transport air data probes. |
| Authors: | Freeman, Paul |
| Keywords: | Aerospace engineering and mechanics |
| Issue Date: | Nov-2011 |
| Abstract: | Air data probes provide essential sensing capabilities to aircraft. The loss or corruption
of air data measurements due to sensor faults jeopardizes an aircraft and its
passengers. To address such faults, sensor hardware redundancy is typically combined
with a voting system to detect and discard erroneous measurements. This approach
relies on redundancy, which may lead to unacceptable increases in system weight and
cost. This thesis presents an alternative, model-based approach to fault detection for
a non-redundant air data system. The model-based fault detection strategy uses robust
linear ltering methods to reject exogenous disturbances, e.g. wind, and provide
robustness to model errors. The proposed algorithm is applied to NASA's Generic
Transport Model aircraft with an air data system modeled based on manufacturer
data provided by Goodrich Sensors and Integrated Systems. The fault detection lter
is designed using linearized models at one
ight condition. The detection performance
is evaluated at a particular reference
ight condition using linear analysis and nonlinear
simulations. Detection performance across the
ight envelope is examined, and
scheduling and blending techniques used to improve detection robustness across an
expanded
ight regime are explored. |
| Description: | University of Minnesota M.S. thesis. Major: Aerospace engineering and mechanics. Advisors: Gary J. Balas, Peter Seiler. 1 computer file (PDF); xii, 87 pages. |
| Permanent URL: | http://purl.umn.edu/119032 |
| Appears in Collections: | Master's Theses (Plan A and Professional Engineering Design Projects)
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Files in This Item:
| File |
Description |
Size | Format |
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| Freeman_Paul_November 2011.pdf | | 2918Kb | PDF | View/Open |
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