DocumentCode
3439516
Title
Notice of Retraction
Uncertainty analysis of turbine blade fatigue life distribution based on Bayesian inference
Author
Jing Li ; Dashuang Luo ; Longlong Zhang ; Shun-Peng Zhu ; Hong-Zhong Huang
Author_Institution
Sch. of Mech., Electron., & Ind. Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu, China
fYear
2013
fDate
15-18 July 2013
Firstpage
1126
Lastpage
1128
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
This paper investigated the application of Bayes´ theorem in life distribution. The uncertainty of the model itself was rarely cared about Bayesian theory. So probability distribution model uncertainty and structural parameter uncertainty was researched through Bayes´ theorem. This paper developed the fatigue life distribution mode of turbine blade. Firstly, find the best model by quantification of fatigue life distribution model; then update the parameters of the model; finally, calculate Bayesian posterior probability by Markov Chain Monte Carlo Algorithm (MCMC) in OpenBUGS.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
This paper investigated the application of Bayes´ theorem in life distribution. The uncertainty of the model itself was rarely cared about Bayesian theory. So probability distribution model uncertainty and structural parameter uncertainty was researched through Bayes´ theorem. This paper developed the fatigue life distribution mode of turbine blade. Firstly, find the best model by quantification of fatigue life distribution model; then update the parameters of the model; finally, calculate Bayesian posterior probability by Markov Chain Monte Carlo Algorithm (MCMC) in OpenBUGS.
Keywords
Bayes methods; Markov processes; Monte Carlo methods; blades; fatigue; turbines; Bayes theorem; Bayesian inference; Bayesian posterior probability; Bayesian theory; Markov chain Monte Carlo algorithm; OpenBUGS; probability distribution model uncertainty; structural parameter uncertainty; turbine blade fatigue life distribution model; uncertainty analysis; Bayes methods; Blades; Data models; Fatigue; Reliability; Turbines; Uncertainty; Bayesian; MCMC; OpenBUGS; life distribution; uncertainty;
fLanguage
English
Publisher
ieee
Conference_Titel
Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE), 2013 International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4799-1014-4
Type
conf
DOI
10.1109/QR2MSE.2013.6625764
Filename
6625764
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