Efficient Dependency Computation for Dynamic Hybrid Bayesian Network in On-line System Health Management Applications

Abstract

This paper presents a new dependency computational algorithm for reliability inference with dynamic hybrid Bayesian network. It features a component-based algorithm and structure to represent complex engineering systems characterized by discrete functional states (including degraded states), and models of underlying physics of failure, with continuous variables. The methodology is designed to be flexible and intuitive, and scalable from small localized functionality to large complex dynamic systems. Markov Chain Monte Carlo (MCMC) inference is optimized using pre-computation and dynamic programming for real-time monitoring of system health. The scope of this research includes new modeling approach, computation algorithm, and an example application for online System Health Management.

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Document Details

Document Type
Technical Report
Publication Date
Oct 02, 2014
Accession Number
AD1002442

Entities

People

  • Ali Mosleh
  • Chonlagarn Iamsumang
  • Mohammad Modarres

Organizations

  • University of Maryland

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Bayesian Networks
  • Computational Complexity
  • Computational Science
  • Dynamic Programming
  • Engineering
  • Failure Mode And Effect Analysis
  • Kalman Filters
  • Markov Chains
  • Mechanical Engineering
  • Monte Carlo Method
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Reliability Engineering
  • Stochastic Processes

Fields of Study

  • Computer science

Readers

  • Distributed Systems and Data Platform Development
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference