Fault Diagnosis Based on Bayesian Networks for the Data Incomplete Industrial System
In the data-incomplete industrial systems,the existing data-driven fault diagnosis techniques cannot be applied di-rectly 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 classi fier 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.
ZHU Jinlin ZHANG Zhengdao
The Key Laboratory of Advanced Process Control for Light Industry,Ministry of Education,Jiangnan University,Wuxi 214122,P.R.China
国际会议
The 30th Chinese Control Conference(第三十届中国控制会议)
烟台
英文
1-5
2011-07-01（万方平台首次上网日期，不代表论文的发表时间）