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Probability based vehicle fault diagnosis : Bayesian network method
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Huang, Yingping, McMurran, Ross, Dhadyalla, Gunwant and Jones, R. Peter. (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 |
| 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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