Fault Detection on Sensors of the Quadrotor System Using Bayesian Network and Two-Stage Kalman Filter †

Tolga Bodrumlu; Fikret Caliskan

doi:10.3390/ecsa-9-13352

Fault Detection on Sensors of the Quadrotor System Using Bayesian Network and Two-Stage Kalman Filter †

Tolga Bodrumlu^*, Fikret Caliskan

^*Corresponding author for this work

Department of Control and Automation Engineering

Istanbul Technical University

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

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/Simulink^TM environment. The results show that the proposed method is capable of detecting faults more robustly.

Original language	English
Article number	33
Journal	Engineering Proceedings
Volume	27
Issue number	1
DOIs	https://doi.org/10.3390/ecsa-9-13352
Publication status	Published - 2022

Bibliographical note

Publisher Copyright:
© 2022 by the authors.

Keywords

Bayesian network
model-based fault diagnosis
two stage Kalman filter
unmanned aerial vehicle

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abstract = "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.",

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T1 - Fault Detection on Sensors of the Quadrotor System Using Bayesian Network and Two-Stage Kalman Filter †

AU - Bodrumlu, Tolga

AU - Caliskan, Fikret

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.

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Fault Detection on Sensors of the Quadrotor System Using Bayesian Network and Two-Stage Kalman Filter †

Abstract

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Keywords

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