A novel fault diagnosis for vehicles based on time-varied Bayesian network modeling
Wenqiang Guo, Zoe Zhu, Yongyan Hou · 2011
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.