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dc.contributor.advisorHealey, Anthony J.
dc.contributor.authorNavarrette, Juan A. III
dc.date1992-Jun
dc.date.accessioned2014-01-29T23:37:46Z
dc.date.available2014-01-29T23:37:46Z
dc.date.issued1992-06
dc.identifier.urihttp://hdl.handle.net/10945/38528
dc.descriptionApproved for public release; distribution is unlimited.en_US
dc.description.abstractThe use of artificial neural networks to provide a method of detecting and isolating impending failures in an autonomous underwater vehivle propulsion system has been studied. Two types of fault diagnostic systems, each capable of detecting different kinds of faults, were designed. The first system addresses the fault identification proces by looking at the raw data available from system sensors. The second design processes sensor data with a Kalman filter before it is input to a neural network. The Kalman filter was designed to identify system parameters that characterize its dynamic response. These parameters serve as input to the network. This system is capable of fault detection, isolation and severity level determination.en_US
dc.description.urihttp://archive.org/details/neuralnetworkbas1094538528
dc.format.extent62 p.;28 cm.en_US
dc.language.isoen_US
dc.publisherMonterey, California. Naval Postgraduate Schoolen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. As such, it is in the public domain, and under the provisions of Title 17, United States Code, Section 105, may not be copyrighted.en_US
dc.titleNeural network based propulsion system fault diagnostics for the NPS AUV IIen_US
dc.typeThesisen_US
dc.contributor.corporateNaval Postgraduate School (U.S.)
dc.contributor.departmentMechanical Engineering
dc.subject.authorFailure detectionen_US
dc.subject.authorSystem parameter identificationen_US
dc.subject.authorAutonomous underwater vehiclesen_US
dc.subject.authorUnderwater vehiclesen_US
dc.subject.authorNeural networken_US
dc.subject.authorautonomousen_US
dc.description.serviceLieutenant, United States Navyen_US
etd.thesisdegree.nameM.S. in Mechanical Engineeringen_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.disciplineMechanical Engineeringen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US


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