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Title: Performance comparison of representative model-based fault reconstruction algorithms for aircraft sensor fault detection and diagnosis
Authors: He, Q
Zhang, W
Lu, P 
Liu, J
Keywords: Inertial measurement unit
Fault detection and diagnosis
Nonlinear disturbance observer
Sliding mode observer
Iterated optimal two-stage extended Kalman filter
Adaptive two-stage extended Kalman filter
Issue Date: 2020
Publisher: Elsevier Masson
Source: Aerospace Science and Technology, 2020, v. 98, 105649 How to cite?
Journal: Aerospace science and technology 
Abstract: 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.
ISSN: 1270-9638
EISSN: 1626-3219
DOI: 10.1016/j.ast.2019.105649
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