Experimental motor fault detection and identification of a quadrotor UAV using a hybrid deep learning approach

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dc.authoridKHANEGHAEI, MOHAMMAD/0009-0006-8295-743X
dc.contributor.authorKhaneghaei, Mohammad
dc.contributor.authorAsadi, Davood
dc.contributor.authorMowla, Md. Najmul
dc.contributor.authorDisken, Gokay
dc.date.accessioned2026-02-27T07:33:01Z
dc.date.available2026-02-27T07:33:01Z
dc.date.issued2025
dc.description.abstractThis study presents a novel experimental hybrid sequential deep learning (DL) approach for real-time motor fault detection and magnitude estimation in quadrotor UAVs, addressing critical gaps in current fault-tolerant control systems. The proposed framework integrates long short-term memory (LSTM) networks with 1D convolutional neural networks (1D-CNN) to enhance fault classification and estimation accuracy. The dual capability distinguishes the proposed model from existing methods, which often focus solely on fault detection without addressing magnitude estimation. A novel dataset, generated through Hardware-in-the-Loop (HIL) experiments, incorporates 25,000 unique fault scenarios under diverse configurations and flight conditions. This dataset strengthens the model's robustness and applicability to real-world scenarios. The developed architecture demonstrated superior performance, achieving 99.2% fault detection accuracy, surpassing existing methods in robustness and efficiency. By embedding the model into a dynamic HIL testbed, the study validates the framework's capability to detect faults, estimate magnitudes, and restore stability in quadrotors under challenging conditions. Experimental results highlight the system's effectiveness in reducing motor anomalies, ensuring improved operational safety and reliability. The approach is adaptable to broader UAV systems, offering significant advancements in autonomous fault-tolerant control.
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [223M312]
dc.description.sponsorshipThis study was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant Number 223M312. The authors thank TUBITAK for their support.
dc.identifier.doi10.1007/s40435-025-01786-4
dc.identifier.issn2195-268X
dc.identifier.issn2195-2698
dc.identifier.issue8
dc.identifier.urihttp://dx.doi.org/10.1007/s40435-025-01786-4
dc.identifier.urihttps://hdl.handle.net/20.500.14669/4420
dc.identifier.volume13
dc.identifier.wosWOS:001536209500002
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherSpringer Nature
dc.relation.ispartofInternational Journal of Dynamics and Control
dc.relation.publicationcategoryMakale - Uluslararas� Hakemli Dergi - Kurum ��retim Eleman�
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20260302
dc.subjectUnmanned aerial vehicle
dc.subjectFault tolerant control algorithm
dc.subjectFault detection and magnitude estimation
dc.subjectDeep neural network
dc.subjectLong short-term memory
dc.subjectHardware-in-the-Loop tests
dc.titleExperimental motor fault detection and identification of a quadrotor UAV using a hybrid deep learning approach
dc.typeArticle

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