NDLI: Predicting the temper embrittlement of steam turbine rotor steels with Bayesian neural network

Content Provider	IEEE Xplore Digital Library
Author	Sheng-Han Zhang Yong-Zhe Fan Ying-Min Chen
Copyright Year	2005
Description	Author affiliation: Sch. of Environ. Sci. & Eng., North China Electr. Power Univ., Baoding (Sheng-Han Zhang; Yong-Zhe Fan; Ying-Min Chen)
Abstract	Bayesian neural network was proposed to predict the temper embrittlement of steam turbine rotor in service. The $FATT_{50}$ (the fracture appearance transition temperature) of the rotors was predicted as a function of ratio of the two peak current densities (I $_{p}/I_{pr}$ ) tested by electrochemical potentiodynamic reaction method, temperature of electrolyte, J-factor and grain size (N). A database was obtained from the test of electrochemical potentiodynamic reaction and Charpy impact. The Bayesian neural network technique was used for modeling of temper embrittlement. The neural network shows a more precise prediction of temper embrittlement of rotor steels than the prediction using multiple linear regression. The training error and verifying error is with the scatter of plusmn20 degC. The results show that, for the temper embrittlement of rotor steels prediction, the prediction model based on Bayesian neural network is feasible and effective
Starting Page	991
Ending Page	994
File Size	626665
Page Count	4
File Format	PDF
ISBN	0780394844
DOI	10.1109/ICIT.2005.1600780
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-12-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Turbines Steel Bayesian methods Neural networks Temperature Testing Current density Grain size Databases Linear regression
Content Type	Text
Resource Type	Article

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