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

摘要：

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 selfadaptive 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.

关键词： rotary machine state identifying torsional oscillation wavelet neural network genetic algorithm

作者: Zong Meng Fengjie Fan Bin Liu

作者单位: Institute of Electrical Engineering Yanshan University Qinhuangdao, Hebei Province, China

会议类型: 国际会议

会议名称: 2010 International Conference on Advanced Mechanical Engineering(2010年先进机械工程国际学术会议 AME 2010)

会议地点: 洛阳

会议语种:英文

页码: 211-217

在线出版日期: 2010-09-04（万方平台首次上网日期，不代表论文的发表时间）