Machine Learning Based Techniques for Failure Detection and Prediction in Unmanned Aerial Vehicle

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dc.authorscopusidAlaa Ali Hameed / 56338374100
dc.authorwosidAlaa Ali Hameed / ABI-8417-2020
dc.contributor.authorMustafa, Ata
dc.contributor.authorJamil, Akhtar
dc.contributor.authorHameed, Alaa Ali
dc.date.accessioned2025-04-18T06:56:27Z
dc.date.available2025-04-18T06:56:27Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractUnmanned aerial vehicles (UAVs) are aircraft with-out human pilot on the board. UAVs have two flight mode: Auto and Manual. In Auto mode, UAV follows a predefined path. The path is embedded in the control system of UAV. In manual mode, human operator in ground control station controls the trajectory of the vehicle remotely. UAVs have diverse applications in military as well as civil sectors. UAVs have to operate in a variety of unseen environments. The diverse usage and uncertainty in operational environment demand safe and reliable operation. Timely identification and rectification of faults stand as a crucial requirement for the operation. One of the major cause of UAV failure is engine fault. In this paper we investigate affectiveness of machine learning techniques regarding engine fault detection and prediction. We analyzed the techniques on AirLab Failure and Anomaly (ALFA) Dataset. For fault detection we used Multi-Layer Perceptron, Random Forest, Support Vector Machine, Ada Boost, Gradient Boosting, Logistic Regression and Single Dimensional Convolutional Neural Network. We observed that Random Forest is most effective technique for fault detection with F-l Score of 0.99. Regarding fault prediction we tried LSTM and GRU based network in different settings. Gated Recurrent Unit performed best with F -1 Score of 0.99 while predicting fault four second ahead of time. © 2024 IEEE.
dc.identifier.citationMustafa, A., Jamil, A., & Hameed, A. A. (2024, April). Machine Learning Based Techniques for Failure Detection and Prediction in Unmanned Aerial Vehicle. In 2024 IEEE 3rd International Conference on Computing and Machine Intelligence (ICMI) (pp. 1-5). IEEE.
dc.identifier.doi10.1109/ICMI60790.2024.10586040
dc.identifier.isbn979-835037297-7
dc.identifier.scopus2-s2.0-85199420437
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6396
dc.indekslendigikaynakScopus
dc.institutionauthorHameed, Alaa Ali
dc.institutionauthoridAlaa Ali Hameed / 0000-0002-8514-9255
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2024 IEEE 3rd International Conference on Computing and Machine Intelligence, ICMI 2024 - Proceedings
dc.relation.publicationcategoryDiğer
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectFailure Detection
dc.subjectFailure Prediction
dc.subjectGRU
dc.subjectLinear Re-gression
dc.subjectLSTM
dc.subjectMachine Learning
dc.subjectRandom Forest
dc.titleMachine Learning Based Techniques for Failure Detection and Prediction in Unmanned Aerial Vehicle
dc.typeOther

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