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- Publisher Website: 10.1109/TCST.2020.3025725
- Scopus: eid_2-s2.0-85112758184
- WOS: WOS:000682140300034
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Article: Air Data Sensor Fault Detection and Diagnosis in the Presence of Atmospheric Turbulence: Theory and Experimental Validation With Real Flight Data
Title | Air Data Sensor Fault Detection and Diagnosis in the Presence of Atmospheric Turbulence: Theory and Experimental Validation With Real Flight Data |
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Authors | |
Keywords | Air data sensor (ADS) fault detection Atmospheric turbulence Disturbances Double-model adaptive estimation (DMAE) Fault detection and diagnosis (FDD) Real flight data |
Issue Date | 2021 |
Publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=87 |
Citation | IEEE Transactions on Control Systems Technology, 2021, v. 29 n. 5, p. 2255-2263 How to Cite? |
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. |
Persistent Identifier | http://hdl.handle.net/10722/303962 |
ISSN | 2023 Impact Factor: 4.9 2023 SCImago Journal Rankings: 2.193 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Lu, P | - |
dc.contributor.author | van Kampen, E | - |
dc.contributor.author | de Visser, C | - |
dc.contributor.author | Chu, Q | - |
dc.date.accessioned | 2021-09-23T08:53:16Z | - |
dc.date.available | 2021-09-23T08:53:16Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | IEEE Transactions on Control Systems Technology, 2021, v. 29 n. 5, p. 2255-2263 | - |
dc.identifier.issn | 1063-6536 | - |
dc.identifier.uri | http://hdl.handle.net/10722/303962 | - |
dc.description.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. | - |
dc.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=87 | - |
dc.relation.ispartof | IEEE Transactions on Control Systems Technology | - |
dc.subject | Air data sensor (ADS) fault detection | - |
dc.subject | Atmospheric turbulence | - |
dc.subject | Disturbances | - |
dc.subject | Double-model adaptive estimation (DMAE) | - |
dc.subject | Fault detection and diagnosis (FDD) | - |
dc.subject | Real flight data | - |
dc.title | Air Data Sensor Fault Detection and Diagnosis in the Presence of Atmospheric Turbulence: Theory and Experimental Validation With Real Flight Data | - |
dc.type | Article | - |
dc.identifier.email | Lu, P: lupeng@hku.hk | - |
dc.identifier.authority | Lu, P=rp02743 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TCST.2020.3025725 | - |
dc.identifier.scopus | eid_2-s2.0-85112758184 | - |
dc.identifier.hkuros | 325323 | - |
dc.identifier.volume | 29 | - |
dc.identifier.issue | 5 | - |
dc.identifier.spage | 2255 | - |
dc.identifier.epage | 2263 | - |
dc.identifier.isi | WOS:000682140300034 | - |
dc.publisher.place | United States | - |