An Integrated Framework for Model-Based Distributed Diagnosis and Prognosis

Abstract

Diagnosis and prognosis are necessary tasks for system reconfiguration and fault-adaptive control in complex systems. Diagnosis consists of detection, isolation and identification of faults, while prognosis consists of prediction of the remaining useful life of systems. This paper presents a novel integrated framework for model-based distributed diagnosis and prognosis, where system decomposition is used to enable the diagnosis and prognosis tasks to be performed in a distributed way. We show how different submodels can be automatically constructed to solve the local diagnosis and prognosis problems. We illustrate our approach using a simulated four-wheeled rover for different fault scenarios. Our experiments show that our approach correctly performs distributed fault diagnosis and prognosis in an efficient and robust manner.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2012
Accession Number
ADA588112

Entities

People

  • Anibal Bregon
  • Indranil Roychoudhury
  • Matthew Daigle

Organizations

  • National Aeronautics and Space Administration

Tags

Communities of Interest

  • Biomedical
  • Cyber
  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Artificial Intelligence
  • Complex Systems
  • Computer Science
  • Control Systems
  • Decomposition
  • Detection
  • Detectors
  • Electrical Engineering
  • Engineering
  • Identification
  • Information Processing
  • Kalman Filters
  • Probability Distributions
  • Systems Engineering
  • United States

Fields of Study

  • Computer science
  • Engineering

Readers

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  • Robotics and Automation.