Sequential Decision Rules for Failure Detection.

Abstract

The formulation of the decision making of a failure detection process as a Bayes sequential decision problem (BSDP) provides a simple conceptualization of the decision rule design problem. As the optimal Bayes rule is not computable, a methodology that is baed on the Baysian approach and aimed at a reduced computational requirement is developed for designing suboptimal rules. A numerical algorithm is constructed to facilitate the design and performance evaluation of these suboptimal rules. The result of applying this design methodology to an example shows that this approach is a useful one. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1981
Accession Number
ADA102025

Entities

People

  • Alan S. Willsky
  • Edward Y. Chow

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • C4I
  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Bayesian Networks
  • Computational Complexity
  • Computational Science
  • Covariance
  • Damage Detection
  • Detection
  • Detectors
  • Failure Mode And Effect Analysis
  • False Alarms
  • Markov Processes
  • Monitoring
  • Monte Carlo Method
  • Probability
  • Sampling
  • Simulations
  • Two Dimensional

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Artificial Intelligence
  • Statistical inference.