Sensor fault detection, isolation, and accommodation via Adaptive Kalman Filter (AKF) algorithm are applied to the lateral dynamics of Boeing-747 aircraft in this study. The flight dynamic model of Boeing-747 aircraft in steady state flight condition is presented and investigated. In nominal case, the Optimal Linear Kalman Filter (OLKF) gives fine estimation values. However, when there is a malfunction on the measurement channels, the accuracy of the filter estimations become poor and the filter becomes unreliable. Two faulty scenarios are investigated. The first scenario comprises the single sensor fault and the second is a simultaneous double sensor fault. The fault detection algorithm detects the fault and isolation process performs via calculating and comparing the statistics of sample and theoretical error variances to distinguish the faulty sensor. Lastly, fault accommodation process is presented in the study as implemented by Adaptive Kalman filter algorithm and demonstrates very efficient, firm, and reliable performance on behalf of enhancing the estimation values of the filter.
Adaptive Kalman Filter-Based Sensor Fault Detection, Isolation, and Accommodation for B-747 Aircraft
Sustainable aviat.
International Symposium on Unmanned Systems and The Defense Industry ; 2021 ; Washington, DC, DC, USA October 26, 2021 - October 28, 2021
2023-06-28
12 pages
Article/Chapter (Book)
Electronic Resource
English
Sensor fault detection , State estimation , Flight control system , Optimal linear Kalman filter , Adaptive Kalman filter , State space model Engineering , Aerospace Technology and Astronautics , Mechanical Engineering , Cyber-physical systems, IoT , Robotics , Computer Applications , Geography, general , Energy
Aircraft Sensor and Actuator Fault Detection, Isolation, and Accommodation
Online Contents | 2011
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