A Novel Fault Diagnosis for Vehicles Based on Time-varied Bayesian Network Modeling
Aiming at one of the key issues in vehicle fault diagnosis underlying time series, modeling the varying diagnosis network structures is investigated in this paper. By incorporating machine learning techniques with the Bayesian network’s advantage of handling the inference in large, noisy and uncertain data, an innovative method based on modeling the varied-time Bayesian network BN for automotive vehicle fault diagnosis is presented. The architecture of an intelligent fault diagnosis system using time-varied Bayesian network modeling is designed, and a fault diagnosis algorithm for vehicles based on time-varied Bayesian network modeling is also advanced. Since the proposed topological model scheme can be modified by learning from the new arriving observation time series data, the inference results under modified BN structures can be improved better. Theoretical analysis about the modeling the network issues are studied in details. The proposed method has been practically applied to model a vehicle engine system. Experimental results demonstrate this automotive fault diagnosis approach based on time-varied Bayesian network modeling is effective and accurate.
Time series Fault diagnosis Bayesian network Modeling
Wenqiang Guo Zoe Zhu Yongyan Hou
School of Electrical and Information Engineering, Shaanxi Univ. of Sci. and Tech., Xi’an, Shaanxi, C Department of Computing Information Science, University of Guelph, Canada, N1G 2W1
国际会议
2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)
四川绵阳
英文
1504-1508
2011-05-23（万方平台首次上网日期，不代表论文的发表时间）