Optimally Robust Redundancy Relations for Failure Detection in Uncertain Systems,

Abstract

All failure detection methods are based, either explictly or implicitly on the use of redundancy, that is on (possibly dynanic) relations among the measured variables. Consequently the robustness of the failure detection process depends to a great degree on the reliability of the redundancy relations given the inevitable presence of model uncertainties. In this paper the authors address the problem of determining redundancy relations which are optimally robust in a sense which includes the major issues of importance in practical failure detection and which provides them with a significant amount of intuition concerning the geometry of robust failure detection. In addition, they provide a procedure involving the construction of a single matrix and the computation of its singular value decomposition, for the determination of a complete sequence of redundancy relations ordered in terms of their level of robustness. This procedure also provides the basis for comparing robust levels of redundancy provided by different sets of sensors. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1983
Accession Number
ADA135949

Entities

People

  • A. S. Willsky
  • G. C. Verghese
  • X. C. Lou

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

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

DTIC Thesaurus Topics

  • Algorithms
  • Computer Programming
  • Computer Science
  • Covariance
  • Damage Detection
  • Decomposition
  • Detection
  • Detectors
  • Difference Equations
  • Eigenvalues
  • Equations
  • Failure Mode And Effect Analysis
  • Mathematical Models
  • Models
  • Nonlinear Programming
  • Optimization
  • Reliability

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Applied Combinatorial Optimization and Logic Circuit Design.
  • Systems Analysis and Design