Neural network based propulsion system fault diagnostics for the NPS AUV II

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Author
Navarrette, Juan A. III
Date
1992-06Advisor
Healey, Anthony J.
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The 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.
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This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.Collections
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