GAO Dang-cheng, YIN Ming-hu, CHEN Guo-ding. The Prediction of Dynamic Coefficients for Tilting-Pad Journal Bearings Based on an AGA-BP Neural Network[J]. International Journal of Plant Engineering and Management, 2014, 19(1): 39-45

The Prediction of Dynamic Coefficients for Tilting-Pad Journal Bearings Based on an AGA-BP Neural Network
GAO Dang-cheng, YIN Ming-hu, CHEN Guo-ding
School of Mechanical Engineering,Northwestern Polytechnical University,Xi'an 710072,P.R. China
To provide real-time dynamic coefficients of tilting-pad journal bearings(TPJBs) for the dynamic analysis of a rotor-bearing system accurately,an improved error back propagation(BP) neural network model is built in this paper.First,the samples are gained by solving the Reynolds equation with the finite differential method based on hydrodynamic lubrication theory.Secondly,the adaptive genetic algorithm(AGA) is applied to optimize the initial weights and thresholds of the BP neural network before training.Then,with a number of trial calculations,the optimum parameters for the neural network are obtained.Finally,an application case of the neural network is given as well as the results analysis.The results show that the AGA can efficiently prevent the training of the neural network from falling into a local minimum,and the AGA-BP neural network of dynamic coefficients for TPJBs built in this paper can meet the demand of engineering.
Key words:    neural network    adaptive genetic algorithm    tilting-pad journal bearing    dynamic coefficients   
Received: 2014-12-10     Revised:
DOI: 10.13434/j.cnki.1007-4546.2014.0106
Corresponding author:     Email:
Author description:
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