Accession Number:

ADA256206

Title:

Neural Network Based Propulsion System Fault Diagnostics for the NPS AUV II

Descriptive Note:

Master's thesis

Corporate Author:

NAVAL POSTGRADUATE SCHOOL MONTEREY CA

Personal Author(s):

Report Date:

1992-06-01

Pagination or Media Count:

68.0

Abstract:

The use of artificial neural networks to provide a method of detecting and isolating impending failures in an autonomous underwater vehicle propulsion system has been studied. Two types of fault diagnostic systems, each capable of detecting different types of faults, were designed. The first system addresses the fault identification process 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 seventy determination.

Subject Categories:

  • Submarine Engineering

Distribution Statement:

APPROVED FOR PUBLIC RELEASE