NDLI: A novel fault diagnosis for vehicles based on time-varied Bayesian network modeling

Content Provider	IEEE Xplore Digital Library
Author	Wenqiang Guo Zoe Zhu Yongyan Hou
Copyright Year	2011
Description	Author affiliation: Department of Computing Information Science, University of Guelph, Canada, N1G 2W1 (Zoe Zhu) \|\| School of Electrical and Information Engineering, Shaanxi Univ. of Sci. and Tech., Xi'an, Shaanxi, China, 710021 (Wenqiang Guo; Yongyan Hou)
Abstract	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.
Starting Page	1504
Ending Page	1508
File Size	231340
Page Count	5
File Format	PDF
ISBN	9781424487370
e-ISBN	9781424487387
DOI	10.1109/CCDC.2011.5968430
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault diagnosis Bayesian methods Computational modeling Time series Data models Mathematical model Modeling Bayesian network Engines Vehicles
Content Type	Text
Resource Type	Article

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