Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/160590
Title: Hardware-in-the-loop simulation for quadrotor fault diagnosis enhancing airworthiness using OS-Fuzzy-ELM
Authors: Thanaraj, T.
Sidharth, Sai
Ng, Bing Feng
Low, Kin Huat
Keywords: Engineering::Aeronautical engineering::Flight simulation
Engineering::Aeronautical engineering::Accidents and air safety
Issue Date: 2022
Source: Thanaraj, T., Sidharth, S., Ng, B. F. & Low, K. H. (2022). Hardware-in-the-loop simulation for quadrotor fault diagnosis enhancing airworthiness using OS-Fuzzy-ELM. 2022 International Conference on Unmanned Aircraft Systems (ICUAS), 263-272. https://dx.doi.org/10.1109/ICUAS54217.2022.9836162
metadata.dc.contributor.conference: 2022 International Conference on Unmanned Aircraft Systems (ICUAS)
Abstract: With the rising adoption of multi-rotor UAVs, it has become ever more crucial that their airworthiness is ensured, especially for hobby-grade UAVs. For some UAVs, their onboard components might not be as reliable as required and as such, faults can occur during flight operations and may develop to cause catastrophe. Hence, these faults need to be accurately diagnosed and quickly mitigated. This paper presents a fault diagnosis model for a quadrotor subjected to partial actuator faults. Flight simulations, with actuator and GPS sensor fault injections, are performed on a hardware-in-the-loop experimental setup to gather flight data consisting of multiple sensors. Based on this data, a preliminary controllability threshold analysis is conducted for the quadrotor. After that, a fault diagnosis model using an online sequential fuzzy-extreme learning machine (OS-Fuzzy-ELM) is trained to locate the actuator faults on the quadrotor UAV. The trained model presents an average testing accuracy and macro-averaged F1 score of 80.2% and 78.0%. A subsequent study to isolate sensor and actuator faults presents the testing accuracy and macro-averaged F1 score to be 1.95% and 1.21%, marginally better than a fault diagnosis model based on a single-layer feedforward network
URI: https://hdl.handle.net/10356/160590
ISBN: 978-1-6654-0593-5
ISSN: 2575-7296
DOI: 10.1109/ICUAS54217.2022.9836162
Schools: School of Mechanical and Aerospace Engineering 
Research Centres: Air Traffic Management Research Institute 
Rights: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/ICUAS54217.2022.9836162.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:ATMRI Conference Papers
MAE Conference Papers

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