Paper Info
Title
Air Data Sensor Fault Detection and Diagnosis in the Presence of Atmospheric Turbulence: Theory and Experimental Validation With Real Flight Data
Abstract
Managing air data sensor fault detection and diagnosis (FDD) in the presence of atmospheric turbulence is challenging since the effects of faults and turbulence are coupled. Existing FDD approaches cannot decouple the faults from the turbulence. To address this challenge, this brief first proposes a novel kinematic model that incorporates the effects of the turbulence. This model is valid inside the entire flight envelope, and there is no need to design a linear parameter varying system. Then, the double-model adaptive estimation algorithm is extended to achieve unbiased state estimation even in the presence of unknown disturbances. The proposed approach is validated using generated turbulence data with various scale lengths and intensities. More importantly, the proposed approach is successfully validated using the real flight test data of a business jet when it is experiencing atmospheric turbulence.
Year
DOI
Venue
2021
10.1109/TCST.2020.3025725
IEEE Transactions on Control Systems Technology
Keywords
DocType
Volume
Air data sensor (ADS) fault detection,atmospheric turbulence,disturbances,double-model adaptive estimation (DMAE),fault detection and diagnosis (FDD),real flight data
Journal
29
Issue
ISSN
Citations 
5
1063-6536
0
PageRank 
References 
Authors
0.34
3
4
Name
Order
Citations
PageRank
Peng Lu112617.62
Erik-Jan van Kampen2153.76
C. C. De Visser373.25
Qiping Chu4123.01