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- Publisher Website: 10.1016/j.ast.2019.105649
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Article: Performance comparison of representative model-based fault reconstruction algorithms for aircraft sensor fault detection and diagnosis
Title | Performance comparison of representative model-based fault reconstruction algorithms for aircraft sensor fault detection and diagnosis |
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Authors | |
Keywords | Sliding mode observer Fault detection and diagnosis Adaptive two-stage extended Kalman filter Nonlinear disturbance observer Iterated optimal two-stage extended Kalman filter Inertial measurement unit |
Issue Date | 2020 |
Citation | Aerospace Science and Technology, 2020, v. 98, article no. 105649 How to Cite? |
Abstract | © 2019 Elsevier Masson SAS This article proposes a nonlinear disturbance observer (NDO) based approach for aircraft inertial measurement unit (IMU) fault detection and diagnosis (FDD) by making use of dynamic and kinematic relations of the aircraft. Furthermore, the detailed aircraft IMU FDD design using four representative fault reconstruction algorithms (NDO, sliding mode observer (SMO), iterated optimal two-stage extended Kalman filter (IOTSEKF) and adaptive two-stage extended Kalman filter (ATSEKF)) is presented. More importantly, this paper presents a thorough FDD performance comparison using these four representative methods. Different FDD performance indexes such as fault detection time, minimum detectable faults and fault estimation errors are compared under various situations such as different fault types and noise standard deviations. The advantages, drawbacks and tuning of each method are investigated, which provide useful insights to aircraft sensor FDD. |
Persistent Identifier | http://hdl.handle.net/10722/288785 |
ISSN | 2023 Impact Factor: 5.0 2023 SCImago Journal Rankings: 1.490 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | He, Qizhi | - |
dc.contributor.author | Zhang, Weiguo | - |
dc.contributor.author | Lu, Peng | - |
dc.contributor.author | Liu, Jinglong | - |
dc.date.accessioned | 2020-10-12T08:05:52Z | - |
dc.date.available | 2020-10-12T08:05:52Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Aerospace Science and Technology, 2020, v. 98, article no. 105649 | - |
dc.identifier.issn | 1270-9638 | - |
dc.identifier.uri | http://hdl.handle.net/10722/288785 | - |
dc.description.abstract | © 2019 Elsevier Masson SAS This article proposes a nonlinear disturbance observer (NDO) based approach for aircraft inertial measurement unit (IMU) fault detection and diagnosis (FDD) by making use of dynamic and kinematic relations of the aircraft. Furthermore, the detailed aircraft IMU FDD design using four representative fault reconstruction algorithms (NDO, sliding mode observer (SMO), iterated optimal two-stage extended Kalman filter (IOTSEKF) and adaptive two-stage extended Kalman filter (ATSEKF)) is presented. More importantly, this paper presents a thorough FDD performance comparison using these four representative methods. Different FDD performance indexes such as fault detection time, minimum detectable faults and fault estimation errors are compared under various situations such as different fault types and noise standard deviations. The advantages, drawbacks and tuning of each method are investigated, which provide useful insights to aircraft sensor FDD. | - |
dc.language | eng | - |
dc.relation.ispartof | Aerospace Science and Technology | - |
dc.subject | Sliding mode observer | - |
dc.subject | Fault detection and diagnosis | - |
dc.subject | Adaptive two-stage extended Kalman filter | - |
dc.subject | Nonlinear disturbance observer | - |
dc.subject | Iterated optimal two-stage extended Kalman filter | - |
dc.subject | Inertial measurement unit | - |
dc.title | Performance comparison of representative model-based fault reconstruction algorithms for aircraft sensor fault detection and diagnosis | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.ast.2019.105649 | - |
dc.identifier.scopus | eid_2-s2.0-85078205348 | - |
dc.identifier.volume | 98 | - |
dc.identifier.spage | article no. 105649 | - |
dc.identifier.epage | article no. 105649 | - |
dc.identifier.isi | WOS:000521508000001 | - |
dc.identifier.issnl | 1270-9638 | - |