Time Bayesian Net Fault Prognostics

Yang Wu; Ting Di Zhao

doi:10.4028/www.scientific.net/AMM.610.350

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 610Time Bayesian Net Fault Prognostics

Time Bayesian Net Fault Prognostics

Abstract:

As increasing in the number of elements and the complexity of their interactions, fault prognostics faces real challenge to predict faults in a complex system. System fault regularly results from the interaction of component faults performing as logical and timing relationships. We use Bayesian Net to evaluate these logical relations. And the other section of system faults concerns about time sequence of those component faults. Thence, this Bayesian method is expanded to Time Bayesian Net in order to solve this kind of problem. Component fault prognostics is the basis, running dates derived from sensors are applied to analyze status of components in real time. Then a traditional Bayesian Net is constructed according to the mechanism and logical structure of the system. Followed by, exploiting the conclusion from interaction analysis of components, this net is built as a Time Bayesian Net. Afterward, Timed Bayesian Net receives inputs from the outcomes of component fault prognostics, and predicts the type of fault and its time of occurrence through Bayes’ rules.

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Periodical:

Applied Mechanics and Materials (Volume 610)

Pages:

350-357

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.610.350

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Online since:

August 2014

Authors:

Yang Wu*, Ting Di Zhao

Keywords:

Bayesian Net, Fault Prognostics, Interaction Analysis, System Fault, System Safety, Time Bayesian Net

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References

[1] Shengkui, Zeng, Pecht, Michael G, Ji, Wu, Status and perspectives of prognostics and health management technologies. Acta Aeronautica et Astronautica Sinica, 2005. 26(5): pp.626-632.

Google Scholar

[2] Ferreiro, Susana, Arnaiz, Aitor, Sierra, Basilio, Irigoien, Itziar, Application of Bayesian networks in prognostics for a new integrated vehicle health management concept. Expert Systems with Applications, 2012. 39(7): pp.6402-6418.

DOI: 10.1016/j.eswa.2011.12.027

Google Scholar

[3] Zhiwei, Liu, Rui, Liu, Jinsong, Xu, Yi, Li, Liming, Zhou, Research of complex system's prognostic and health management. Computer Measurement & Control, 2010. 18(12): pp.2687-2689, 2751.

Google Scholar

[4] Wang, Shi, Yufeng, Sun, Zili, Wang, Guangyan, Zhao, A study of PHM system and its fault forecasting model. Fire Control & Command Control, 2009. 34(10): pp.29-32, 35.

Google Scholar

[5] Glymour, Clark, Learning, prediction and causal Bayes nets. Trends in Cognitive Sciences, 2003. 7(1): pp.43-48.

DOI: 10.1016/s1364-6613(02)00009-8

Google Scholar

[6] Caines, P.E., R. Deardon and H.P. Wynn, Bayes nets of time series: Stochastic Realizations and Projections. 2009. 28: pp.155-166.

DOI: 10.1007/978-0-387-79936-0_7

Google Scholar

[7] Guoping, Jiang, Yingwu, Chen, Temporal Bayesian network and probability inference. Journal of Management Sciences in China, 2007. 10(2): pp.12-18.

Google Scholar