Adaptive Hybrid Unscented Kalman Filter for Aircraft Sensor Fault Detection, Isolation and Reconstruction

Lu, Peng; Eykeren, Laurens Van; Kampen, E. van; Chu, Q.P.; Yu, Bin

doi:10.2514/6.2014-1145

Cited by 29 publications

(18 citation statements)

References 31 publications

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“…This measurement update is different from that of the normal UKF [22,27], which is given next for comparison and quick reference:…”

Section: A Robust Three-step Unscented Kalman Filtermentioning

confidence: 99%

“…(19)(20)(21)(22)(23)(24). If f 0, the measurements are only corrupted by white Gaussian noises, as can be seen from the equations.…”

Section: A Real-life Measurement Modelmentioning

confidence: 99%

“…Alternatively, the kinematic model can be used to detect the faults in the ADSs, thereby reducing the influence of model uncertainties caused by the calculation of the aerodynamic forces and moments [21,22]. Van Eykeren and Chu [23] use an adaptive extended Kalman filter to detect the faults in the ADSs.…”

mentioning

confidence: 99%

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Adaptive Three-Step Kalman Filter for Air Data Sensor Fault Detection and Diagnosis

Eykeren

Kampen

et al. 2016

Journal of Guidance, Control, and Dynamics

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“…This measurement update is different from that of the normal UKF [22,27], which is given next for comparison and quick reference:…”

Section: A Robust Three-step Unscented Kalman Filtermentioning

confidence: 99%

“…(19)(20)(21)(22)(23)(24). If f 0, the measurements are only corrupted by white Gaussian noises, as can be seen from the equations.…”

Section: A Real-life Measurement Modelmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Three-Step Kalman Filter for Air Data Sensor Fault Detection and Diagnosis

Eykeren

Kampen

et al. 2016

Journal of Guidance, Control, and Dynamics

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“…However, once the star sensor fails, the gyro drift obtained by this method will deviate from reality greatly, which can lead to the inaccurate diagnosis results. To improve the robustness of the FDI method, innovation-based adaptive KF or residual-based adaptive estimation has been explored in [20][21][22]. FDI results demonstrate the effectiveness of these methods.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for Space-Borne Fiber-Optic Gyroscopes Using a Hybrid Method

2020

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Timely detection and isolation of the failures in attitude sensor is a critical task to ensure the normal operation of the satellite. This paper proposes a fault detection and isolation scheme for the fouraxis fiber-optic gyroscopes using a hybrid method. Based on the attitude kinematics and gyro model, a sensor fault detector is developed using an adaptive Kalman filter. The process noise covariance can be autonomously tuned by the proposed adaptive mechanism, which makes the filter more robust to the uncertain covariance parameters. The constant drift estimations obtained by the adaptive Kalman filter are applied to detect sensor faults. At the same time, the value of parity equation is computed in real-time using the signals of the four-axis gyroscopes. Then fault diagnosis rules are presented to isolate the faulty sensor. Using the proposed hybrid fault detection and isolation method, fault in gyroscopes can be detected and isolated reliably, and the fault in angle sensor can be detected as well. Thus the results of fault detection and isolation provide the criterions for fiber-optic gyroscopes reconstruction. Finally, the effectiveness of the developed scheme is demonstrated by numerical simulations of four-axis gyroscopes and star sensor with some typical faults. INDEX TERMS Space-borne fiber-optic gyroscopes, fault detection and isolation, adaptive Kalman filter, parity equation, reconstruction.

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“…Normality tests are performed on past innovation errors within a moving window to detect a fault [11], [12]. Threshold tests on the innovation errors have also been implemented [5], [13].…”

Section: Introductionmentioning

confidence: 99%

A Robust State Estimation Method for Unknown, Time-Varying and Featureless Aircraft Sensor Failures

et al. 2020

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Fault-tolerant state estimation is necessary for analytical redundancy in aviation safety, but complicated fault conditions pose a major challenge to reliable estimations. In this paper, we propose a new state-estimation method based on an intermittent-measurement Kalman filter, a maximum-likelihood estimation rule, and the Gaussian mixture reduction. This method is robust to time-varying and featureless faults, and requires no past history of innovation errors to perform fault detection and isolation, leading to the easy implementation and improved accuracy under severe sensor-failure conditions. With an example application of monitoring the rotation speeds of a two-spool jet engine in simulations, the effectiveness of the proposed method is verified.

show abstract

Adaptive Hybrid Unscented Kalman Filter for Aircraft Sensor Fault Detection, Isolation and Reconstruction

Cited by 29 publications

References 31 publications

Adaptive Three-Step Kalman Filter for Air Data Sensor Fault Detection and Diagnosis

Adaptive Three-Step Kalman Filter for Air Data Sensor Fault Detection and Diagnosis

Fault Diagnosis for Space-Borne Fiber-Optic Gyroscopes Using a Hybrid Method

A Robust State Estimation Method for Unknown, Time-Varying and Featureless Aircraft Sensor Failures

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