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

Research output: Contribution to journalArticlepeer-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/SimulinkTM environment. The results show that the proposed method is capable of detecting faults more robustly.

Original languageEnglish
Article number33
JournalEngineering Proceedings
Volume27
Issue number1
DOIs
Publication statusPublished - 2022

Bibliographical note

Publisher Copyright:
© 2022 by the authors.

Funding

This research was financially supported by Istanbul Technical University, grant number 42754.

FundersFunder number
Istanbul Teknik Üniversitesi42754

    Keywords

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

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