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

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.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1992
Accession Number
ADA256206

Entities

People

  • Juan A. Navarrete Iii

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Autonomous Underwater Vehicles
  • California
  • Computer Simulations
  • Damage Detection
  • Detection
  • Detectors
  • Dynamic Response
  • Engineers
  • Estimators
  • Failure Mode And Effect Analysis
  • Identification
  • Kalman Filters
  • Naval Architecture
  • Neural Networks
  • Propulsion Systems
  • Test And Evaluation

Readers

  • Inertial Navigation Systems.
  • Robotics and Automation.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Neural Networks