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Supervised Learning Classification Applications in Fault Detection and Diagnosis: An Overview of Implementations in Unmanned Aerial Systems
- Majd Saied - Lebanese University, Faculty of Engineering, Scientific Research Center in Engineering, Lebanon ,
- Hadi Attieh - Lebanese University, Faculty of Engineering, Scientific Research Center in Engineering, Lebanon ,
- Hussein Mazeh - Lebanese University, Faculty of Engineering, Scientific Research Center in Engineering, Lebanon ,
- Hassan Shraim - Lebanese University, Faculty of Engineering, Scientific Research Center in Engineering, Lebanon ,
- Clovis Francis - Lebanese University, Faculty of Engineering, Scientific Research Center in Engineering, Lebanon
Journal Article
01-16-01-0004
ISSN: 1946-3855, e-ISSN: 1946-3901
Sector:
Topic:
Citation:
Saied, M., Attieh, H., Mazeh, H., Shraim, H. et al., "Supervised Learning Classification Applications in Fault Detection and Diagnosis: An Overview of Implementations in Unmanned Aerial Systems," SAE Int. J. Aerosp. 16(1):57-73, 2023, https://doi.org/10.4271/01-16-01-0004.
Language:
English
Abstract:
Statistical machine learning classification methods have been widely used in the
fault detection analysis in several engineering domains. This motivates us to
provide in this article an overview on the application of these methods in the
fault diagnosis strategies and also their successful use in unmanned aerial
vehicles (UAVs) systems. Different existing aspects including the implementation
conditions, offline design, and online computation algorithms as well as
computation complexity and detection time are discussed in detail. Evaluation
and validation of these aspects have been ensured by a simple demonstration of
the basic classification methods and neural network techniques in solving the
fault detection and diagnosis problem of the propulsion system failure of a
multirotor UAV. A testing platform of an Hexarotor UAV is completely realized.
Measurements data issued from the onboard sensors are collected and a
classification model to detect damaged propellers and failed motors has been
built. To simulate a motor fault condition, its effectiveness is reduced using
an RC transmitter. Propeller damages are simulated by clipping the propellers
gradually. Experimental results demonstrate that artificial neural networks
(ANN) techniques outperform other methods in terms of classification accuracy
and are shown to be effective at identifying the different types of damaged
propellers or failed motors.