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Probability based vehicle fault diagnosis : Bayesian network method

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Huang, Yingping, McMurran, Ross, Dhadyalla, Gunwant and Jones, P. (Peter), Dr.. (2008) Probability based vehicle fault diagnosis : Bayesian network method. Journal of Intelligent Manufacturing, Vol.19 (No.3). pp. 301-311. ISSN 0956-5515

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Official URL: http://dx.doi.org/10.1007/s10845-008-0083-7

Abstract

Fault diagnostics are increasingly important for ensuring vehicle safety and reliability. One of the issues in vehicle fault diagnosis is the difficulty of successful interpretation of failure symptoms to correctly diagnose the real root cause. This paper presents an innovative Bayesian Network based method for guiding off-line vehicle fault diagnosis. By using a vehicle infotainment system as a case study, a number of Bayesian diagnostic models have been established for fault cases with single and multiple symptoms. Particular considerations are given to the design of the Bayesian model structure, determination of prior probabilities of root causes, and diagnostic procedure. In order to unburden the computation, an object oriented model structure has been adopted to prevent the model from overly large. It is shown that the proposed method is capable of guiding vehicle diagnostics in a probabilistic manner. Furthermore, the method features a multiple-symptoms-orientated troubleshooting strategy, and is capable of diagnosing multiple symptoms optimally and simultaneously.

Item Type:

Journal Article

Subjects:

Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
T Technology > TS Manufactures

Divisions:

Faculty of Science > Engineering
Faculty of Science > WMG (Formerly the Warwick Manufacturing Group)

Library of Congress Subject Headings (LCSH):

Bayesian statistical decision theory -- Diagnostic use, Bayesian statistical decision theory -- Computer programs, Motor vehicles -- Testing, Fault location (Engineering)

Journal or Publication Title:

Journal of Intelligent Manufacturing

Publisher:

Springer New York LLC

ISSN:

0956-5515

Official Date:

June 2008

Dates:

Date	Event
June 2008	Published

Volume:

Vol.19

Number:

No.3

Number of Pages:

Page Range:

pp. 301-311

Identification Number:

10.1007/s10845-008-0083-7

Status:

Peer Reviewed

Publication Status:

Published

Access rights to Published version:

Restricted or Subscription Access

Funder:

Advantage West Midlands (AWM), Jaguar PLC

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