A neural network approach to failure diagnostics for underwater vehicles
Abstract
This paper addresses the proposed use of Kalman filters
and Artificial Neural Networks to provide the detection, and
isolation of impending system failures. Such system health
diagnosis is necessary to the overall success of mission controllers
for AUVs. Two examples of network designs are given. The
first addresses the identification of anomalous changes to the
vehicle's acceleration behavior resulting from possible propulsion
system changes of loss of propulsion efficiency from fouling.
The second example relates to the identification of excessive
frictional loads in the propulsion drive train that may cause motor
failure. In each case, the training method and the resulting
decision surface characterization of the networks so designed are
given.
Rights
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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