Fault diagnosis with continuous system models

Author

Chu, Bei-Tseng Bill

Author_Institution

Dept. of Comput. Sci., North Carolina Univ., Charlotte, NC, USA

Volume

23

Issue

1

fYear

1993

Firstpage

55

Lastpage

64

Abstract

A unified diagnostic reasoning model that deals with both continuous as well as discrete causal relationships is presented. The diagnostic model significantly extends the formal probabilistic diagnostic reasoning models of other works. Statistical theories are used to formally derive conditional causation probabilities based on continuous system models. The derived conditional causation probabilities can be used along with discrete causal relationships provided by experts to find the most probable diagnostic hypothesis for a given set of observations

Keywords

diagnostic expert systems; model-based reasoning; probability; conditional causation probabilities; continuous causal relationships; continuous system models; diagnostic reasoning model; discrete causal relationships; fault diagnosis; most probable diagnostic hypothesis; Artificial intelligence; Computer errors; Computer science; Continuous time systems; Fault diagnosis; Instruments; Noise measurement; Probability; Random variables; Volume measurement;

fLanguage

English

Journal_Title

Systems, Man and Cybernetics, IEEE Transactions on

Publisher

ieee

ISSN

0018-9472

Type

jour

DOI

10.1109/21.214767

Filename

214767