Probabilistic graphical models for the diagnosis of analog electrical circuits

Authors:
Christian Borgelt;Rudolf Kruse
Affiliations:
School of Computer Science, University of Magdeburg, Magdeburg, Germany;School of Computer Science, University of Magdeburg, Magdeburg, Germany
Venue:
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Year:
2005

Citing 6
Cited 0

Diagnosing multiple faults

Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
On the effective implementation of the iterative proportional fitting procedure

Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
Introduction to Bayesian Networks

Introduction to Bayesian Networks
Expert Systems and Probabiistic Network Models

Expert Systems and Probabiistic Network Models
Graphical Models: Methods for Data Analysis and Mining

Graphical Models: Methods for Data Analysis and Mining

Quantified Score

Hi-index	0.00

Visualization

Abstract

We describe an algorithm to build a graphical model—more precisely: a join tree representation of a Markov network—for a steady state analog electrical circuit. This model can be used to do probabilistic diagnosis based on manufacturer supplied information about nominal values of electrical components and their tolerances as well as measurements made on the circuit. Faulty components can be identified by looking for high probabilities for values of characteristic magnitudes that deviate from the nominal values.