By Topic

Fault diagnosis based on bayesian networks for the data incomplete industrial system

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Zhu Jinlin ; Key Lab. of Adv. Process Control for Light Ind., Jiangnan Univ., Wuxi, China ; Zhang Zhengdao

In the data-incomplete industrial systems, the existing data-driven fault diagnosis techniques cannot be applied directly due to the missing of sampled data. In this paper, we propose a method based on Bayesian networks to realize the fault diagnosis of systems with incomplete sample data. Our method uses the Expectation-Maximization (EM) algorithm to estimate the missing part of incomplete sample data, then selects the features based on the mutual information technique, and finally, constructs the Bayesian network classifier to achieve the fault diagnosis of systems. We used the Tennessee Eastman Process as the simulation model, and analyzed the diagnostic performance under different degrees of missing data. Both the normal case and three faults had been considered in the simulation. Compared with the data-complete case, our method achieved a good diagnosis performance in the case within 10% rate of missing sample data.

Published in:

Control Conference (CCC), 2011 30th Chinese

Date of Conference:

22-24 July 2011