TY - JOUR
T1 - Fault Detection on Sensors of the Quadrotor System Using Bayesian Network and Two-Stage Kalman Filter †
AU - Bodrumlu, Tolga
AU - Caliskan, Fikret
N1 - Publisher Copyright:
© 2022 by the authors.
PY - 2022
Y1 - 2022
N2 - In recent years, model-based fault techniques have become popular due to their capability to reduce calculation cost. Bayesian Network and two-stage Kalman filter-based methods have recently become quite popular due to their robustness. In this paper, a model-based fault diagnosis method is presented that uses a Bayesian network and two-stage Kalman filter (TSKF) together to robustly determine the sensor faults in an Unmanned Aerial Vehicle (UAV) system. By using these two approaches together, the robustness of the fault detection in the sensor improved. For demonstrating the behavior of the proposed method, numerical simulations were performed in MATLAB/SimulinkTM environment. The results show that the proposed method is capable of detecting faults more robustly.
AB - In recent years, model-based fault techniques have become popular due to their capability to reduce calculation cost. Bayesian Network and two-stage Kalman filter-based methods have recently become quite popular due to their robustness. In this paper, a model-based fault diagnosis method is presented that uses a Bayesian network and two-stage Kalman filter (TSKF) together to robustly determine the sensor faults in an Unmanned Aerial Vehicle (UAV) system. By using these two approaches together, the robustness of the fault detection in the sensor improved. For demonstrating the behavior of the proposed method, numerical simulations were performed in MATLAB/SimulinkTM environment. The results show that the proposed method is capable of detecting faults more robustly.
KW - Bayesian network
KW - model-based fault diagnosis
KW - two stage Kalman filter
KW - unmanned aerial vehicle
UR - http://www.scopus.com/inward/record.url?scp=85145408500&partnerID=8YFLogxK
U2 - 10.3390/ecsa-9-13352
DO - 10.3390/ecsa-9-13352
M3 - Article
AN - SCOPUS:85145408500
SN - 2673-4591
VL - 27
JO - Engineering Proceedings
JF - Engineering Proceedings
IS - 1
M1 - 33
ER -