Abstract/Details

Development and evaluation of a fault detection and identification scheme for the WVU YF-22 UAV using the artificial immune system approach


2009 2009

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Abstract (summary)

A failure detection and identification (FDI) scheme is developed for a small remotely controlled jet aircraft based on the Artificial Immune System (AIS) paradigm. Pilot-in-the-loop flight data are used to develop and test a scheme capable of identifying known and unknown aircraft actuator and sensor failures. Negative selection is used as the main mechanism for self/non-self definition; however, an alternative approach using positive selection to enhance performance is also presented. Tested failures include aileron and stabilator locked at trim and angular rate sensor bias. Hyper-spheres are chosen to represent detectors. Different definitions of distance for the matching rules are applied and their effect on the behavior of hyper-bodies is discussed. All the steps involved in the creation of the scheme are presented including design selections embedded in the different algorithms applied to generate the detectors set. The evaluation of the scheme is performed in terms of detection rate, false alarms, and detection time for normal conditions and upset conditions. The proposed detection scheme achieves good detection performance for all flight conditions considered. This approach proves promising potential to cope with the multidimensional characteristics of integrated/comprehensive detection for aircraft sub-system failures.

A preliminary performance comparison between an AIS based FDI scheme and a Neural Network and Floating Threshold based one is presented including groundwork on assessing possible improvements on pilot situational awareness aided by FDI schemes. Initial results favor the AIS approach to FDI due to its rather undemanding adaptation capabilities to new environments. The presence of the FDI scheme suggests benefits for the interaction between the pilot and the upset conditions by improving the accuracy of the identification of each particular failure and decreasing the detection delays.

Indexing (details)


Subject
Aerospace engineering
Classification
0538: Aerospace engineering
Identifier / keyword
Applied sciences
Title
Development and evaluation of a fault detection and identification scheme for the WVU YF-22 UAV using the artificial immune system approach
Author
Sanchez, Sebastian Pablo
Number of pages
126
Publication year
2009
Degree date
2009
School code
0256
Source
MAI 48/02M, Masters Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
ISBN
9781109450835
Advisor
Perhinschi, Mario G.
University/institution
West Virginia University
University location
United States -- West Virginia
Degree
M.S.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
1471810
ProQuest document ID
305033817
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/305033817
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