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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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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
Volume: Vol.19
Number: No.3
Number of Pages: 11
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
References:

Foran, T., & Jackman, B. (2005). An intelligent diagnostic system
for distributed, muti-ECU automotive control systems. SAE paper
2005–01–1444, SAE World Congress.
Gelgele, H. L., & Wang, K. (1998). An expert system for engine
fault diagnosis—development and application. Journal of Intelligent
Manufacturing, 9, 539–545.
Heckerman, D., Breese, J. S., & Rommelse, K. (1995). Decisiontheoretic
troubleshooting. Communication of the ACM, 38(3),
49–57.
Huang, Y., Antory, D., Jones, P., & Groom, C. (2006). Bayesian belief
network based fault diagnosis in automotive electronic systems. In
Proceeding of 8th International Symposium on Advanced Vehicle
Control (pp. 469–473). August 20∼24, 2006, Taipei, Taiwan.
Kahn Jr, C. E., Roberts, L. K., Shaffer, K. A., & Haddawy, P. (1997).
Construction of a Bayesian network for mammographic diagnosis
of breast cancer. Computers in biology and medicine, 27(1), 19–29.
Kang, C.W., & Golay, M.W. (1999). A Bayesian belief network-based
advisory system for operational availability focused diagnosis of
complex nuclear power systems. Expert Systems with Applications,
27, 21–32.
Neil, M., Fenton, N., Forey, S., & Harris, R. (2001). Using Belief Networks
to Predict the reliability of military vehicles. Computer and
Control Engineering Journal, 12(1), 11–20.
Neil, M., Fenton, N., & Nielsen, L. (2000). Building large-scale Bayesian
networks. Knowledge Engineering Review, 15(3), 257–284.
Wang, X., Zheng, B., Good, W. F., King, J. L., & Chang, Y. (1999).
Computer-assisted diagnosis of breast cancer using a data-driven
Bayesian belief network. International Journal of Medical Informatics,
54, 115–126.
Wolbrecht, E., D’Ambrosio, B., Paasch, R., & Kirby, D. (2000).
Monitoring and diagnosis of a multistage manufacturing
process using Bayesian network, Artificial Intelligence
for Engineering Design. Analysis and Manufacturing, 14,
53–67.
URI: http://wrap.warwick.ac.uk/id/eprint/30022

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