...
首页> 外文期刊>Shock and vibration >HOC Based Blind Identification of Hydroturbine Shaft Volterra System
【24h】

HOC Based Blind Identification of Hydroturbine Shaft Volterra System

机译:基于HOC的水轮机轴系Volterra系统的盲辨识。

获取原文
获取原文并翻译 | 示例

摘要

In order to identify the quadratic Volterra system simplified from the hydroturbine shaft system, a blind identification method based on the third-order cumulants and a reversely recursive method are proposed. The input sequence of the system under consideration is an unobservable independent identically distributed (i.i.d.), zero-mean and non-Gaussian stationary signal, and the observed signals are the superposition of the system output signal and Gaussian noise. To calculate the third-ordermoment of the output signal, a computer loop judgment method is put forward to determine the coefficient. When using optimization method to identify the time domain kernels, we combined the traditional optimization algorithm (direct search method) with genetic algorithm (GA) and constituted the hybrid genetic algorithm (HGA). Finally, according to the prototype observation signal and the time domain kernel parameters obtained from identification, the input signal of the system can be gained recursively. To test the proposed method, three numerical experiments and engineering application have been carried out. The results show that the method is applicable to the blind identification of the hydroturbine shaft system and has strong universality; the input signal obtained by the reversely recursive method can be approximately taken as the random excitation acted on the runner of the hydroturbine shaft system.
机译:为了识别从水轮机竖井系统简化的二次方Volterra系统,提出了一种基于三阶累积量的盲辨识方法和反向递归方法。所考虑系统的输入序列是不可观测的独立均匀分布(i.i.d.),零均值和非高斯平稳信号,并且观察到的信号是系统输出信号和高斯噪声的叠加。为了计算输出信号的三阶矩,提出了一种计算机环路判断方法来确定系数。当使用优化方法识别时域内核时,我们将传统的优化算法(直接搜索方法)与遗传算法(GA)结合起来,构成了混合遗传算法(HGA)。最后,根据原型观测信号和识别得到的时域内核参数,可以递归获得系统的输入信号。为了验证该方法,进行了三个数值实验和工程应用。结果表明,该方法适用于水轮机竖井系统的盲目识别,具有很强的通用性。通过反向递归方法获得的输入信号可以近似地视为作用在水轮机轴系转轮上的随机激励。

著录项

  • 来源
    《Shock and vibration 》 |2017年第1期| 6732704.1-6732704.11| 共11页
  • 作者

    Bai Bing; Zhang Lixiang;

  • 作者单位

    North China Univ Water Resources & Elect Power, Sch Civil Engn & Commun, Beihuan Rd 36, Zhengzhou 450045, Henan, Peoples R China;

    Kunming Univ Sci & Technol, Dept Engn Mech, South Jingming Rd 727, Kunming 650500, Yunnan, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号