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Experimental evaluation of data fusion algorithm for residual generation in detecting UAV servo actuator fault

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dc.citedby1
dc.contributor.authorSahwee Z.en_US
dc.contributor.authorRahman N.A.en_US
dc.contributor.authorSahari K.S.M.en_US
dc.contributor.authorid55524079500en_US
dc.contributor.authorid9338388000en_US
dc.contributor.authorid57218170038en_US
dc.date.accessioned2023-05-29T06:00:14Z
dc.date.available2023-05-29T06:00:14Z
dc.date.issued2015
dc.descriptionAlgorithms; Data fusion; Hardware; Military vehicles; Redundancy; Software reliability; Unmanned aerial vehicles (UAV); Analytical redundancy; Data fusion algorithm; Experimental evaluation; Fault detection algorithm; Hostile environments; Model reference methods; On-board fault detection; Software and hardwares; Fault detectionen_US
dc.description.abstractUnmanned Aerial Vehicles used by military are generally designed with very high reliability and have multiple redundancy of software and hardware equipment because they are intended to operate in hostile environment. But, relatively low cost UAVs used commercially are not equipped with such systems. Usually, micro UAVs weight less than 2kg are equipped with on-board miniature sensor and operate without any hardware redundancy and thus could reduce their reliability. Some of these commercial UAVs that operate in populated areas will cause damage and fatality if faulty system occurred. Hence there is a need for on-board fault detection and isolation system without degradating the UAV flyability and its cost. Analytical redundancy or model reference method of fault detection algorithms could be implemented as most UAVs are microcontroller controlled. Together, with the availability of miniature sensors could provide an ideal platform for implementing fault detection. In this paper, the development of fault detection through residual generation algorithm is implemented with data fusion from miniature sensors. Some of these sensors are already installed within the autopilot system which reduce the amount of additional sensors needed. Identification of fault in the elevator is simulated experimentally and fault detection rate is monitored. From the implemented algorithm, the data fusion from additional sensors shows improvement in fault detection rate.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1260/1756-8293.7.2.133
dc.identifier.epage145
dc.identifier.issue2
dc.identifier.scopus2-s2.0-84951041534
dc.identifier.spage133
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84951041534&doi=10.1260%2f1756-8293.7.2.133&partnerID=40&md5=d752cb4da964a085870b8ff465e9bd7c
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22326
dc.identifier.volume7
dc.publisherMulti-Science Publishing Co. Ltden_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleInternational Journal of Micro Air Vehicles
dc.titleExperimental evaluation of data fusion algorithm for residual generation in detecting UAV servo actuator faulten_US
dc.typeArticleen_US
dspace.entity.typePublication
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