Study on State Identifying of Rotary Machine Torsional Oscillation Based on Wavelet Neural Network

Abstract:

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This article established a new combining hierarchy genetic algorithm and multivariate linear regression model of WNN (wavelet neural network) for identify the feature of rotary machine. The effection on the question of nonlinear approximation is verified through the simulation and optimization. The test datas of a tandem mill are inputted into the model. After trained, the model has automatic ability of obtained the inspect information and the ability of adapt the changing of worked condition. The self-adaptive study and diagnosis of torsional oscillation state on different work condition are realized. The results verify the combining hierarchy genetic algorithm and multivariate linear regression model has the reliability.

Info:

Periodical:

Edited by:

Zhenyu Du and Bin Liu

Pages:

211-217

DOI:

10.4028/www.scientific.net/AMM.26-28.211

Citation:

Z. Meng et al., "Study on State Identifying of Rotary Machine Torsional Oscillation Based on Wavelet Neural Network", Applied Mechanics and Materials, Vols. 26-28, pp. 211-217, 2010

Online since:

June 2010

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Price:

$35.00

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