A Bayesian Approach to the Design of Decision Rules for Failure Detection and Identification,

Abstract

The formulation of the decision making process of a failure detection algorithm as a Bayes sequential decision problem provides a simple conceptualization of the decision rule design problem. As the optimal Bayes rule is not computable, a methodology that is based on the Bayesian approach and aimed at a reduced computational requirement is developed for disigning 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 potentially a useful one.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Feb 14, 1983
Accession Number
ADA126899

Entities

People

  • Alan S. Willsky
  • Edward Y. Chow

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Bayesian Networks
  • Computational Complexity
  • Computational Science
  • Damage Detection
  • Detection
  • Detectors
  • Failure Mode And Effect Analysis
  • False Alarms
  • Identification
  • Markov Processes
  • Mathematical Filters
  • Monitoring
  • Monte Carlo Method
  • Probability
  • Sequences
  • Statistics

Readers

  • Fault Tolerant Diagnosis of Black and White Balloon Isolation Tests Using ¥.
  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms