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Rotor-bearing system identification using time domain methods.

机译:使用时域方法识别转子轴承系统。

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摘要

Several existing and new time domain methods have been compared and proposed for rotor bearing system off-line and online modal parameter and physical parameter identification. This dissertation concentrates on modal and physical parameter identification with incomplete input measurement, single input and single output measurement, or output measurement only at limited locations. This kind of measurement environment is typical for current rotating machinery. The transient analysis is thus important for the identification and it has been shown that increased input frequency change will induce large transient signals which can be used for system identification with output measurement only.; Existing modal identification methods are reviewed. Among them, the Backward ARMA method is found to be most suitable for modal parameter identification with single input and single output measurement. A new method, Extended Backward ARMA, is derived to identify system zeros and automatically detect the number of system zeros. The existing modal identification methods for output measurement only case, i.e. Random Decrement and Yule-Walker Equation, need to be modified to handle the rotating machinery online measurement condition. Three new identification methods, i.e. Direct AR Modeling, Improved Random Impulse Injection, and Improved Yule Walker Equation, have been proposed for identification with the rotating machinery online measurement condition and with output measurement only. The Direct AR Modeling has been shown to be very effective for the modal parameters identification under certain circumstances. Improved Random Impulse Injection and Improved Yule Walker Equation are shown to be able to reduce the required averaging number, to have higher frequency resolution, and to be able to identify well-damped modes.; The physical parameter identification methods with complete input and output information are reviewed. Two new methods, i.e. Modified Newton-Raphson method and Transformed Eigenvalue method, have been proposed for Inverse Eigenvalue analysis. Modified Newton Raphson method takes into account the unique nature of rotor bearing system. Transformed Eigenvalue method is proven to be more computationally efficient, have larger convergence range. These new methods not only apply to physical parameter identification with complete input and output information but also apply to physical parameter identification with output measurement only.; The combination of above mentioned identification methods for modal parameters and physical parameters with or without the complete knowledge of input signals is proposed for online and off-line rotor bearing system identification.
机译:比较了几种现有的和新的时域方法,并提出了转子轴承系统离线和在线模态参数以及物理参数识别的方法。本文主要研究模态和物理参数的识别,包括不完整的输入测量,单输入和单输出测量或仅在有限位置的输出测量。对于当前的旋转机械来说,这种测量环境是典型的。因此,瞬态分析对识别很重要,并且已经表明,输入频率变化的增加会引起大的瞬态信号,这些信号只能用于输出测量的系统识别。审查了现有的模态识别方法。其中,发现Backward ARMA方法最适合于具有单输入和单输出测量的模态参数识别。派生出一种新方法,即扩展后向ARMA,以识别系统零并自动检测系统零的数量。仅用于输出测量情况的现有模态识别方法,即随机减量和Yule-Walker方程,需要修改以处理旋转机械在线测量条件。提出了三种新的识别方法,即直接AR建模,改进的随机脉冲注入和改进的Yule Walker方程,用于通过旋转机械在线测量条件和仅通过输出测量进行识别。在某些情况下,直接AR建模对于模态参数识别非常有效。改进的随机脉冲注入和改进的Yule Walker方程显示出能够减少所需的平均数,具有更高的频率分辨率并能够识别阻尼良好的模式。回顾了具有完整输入和输出信息的物理参数识别方法。提出了两种新方法,即改进牛顿-拉夫森法和变换特征值法进行逆特征值分析。改进的Newton Raphson方法考虑了转子轴承系统的独特性。事实证明,变换特征值方法计算效率更高,收敛范围更大。这些新方法不仅适用于具有完整输入和输出信息的物理参数识别,而且还适用于仅具有输出测量的物理参数识别。提出了上面提到的模态参数和物理参数的识别方法的组合,无论是否具有完整的输入信号知识,都可以用于在线和离线转子轴承系统的识别。

著录项

  • 作者

    Zhong, Ping.;

  • 作者单位

    University of Virginia.;

  • 授予单位 University of Virginia.;
  • 学科 Engineering Mechanical.
  • 学位 Ph.D.
  • 年度 1997
  • 页码 353 p.
  • 总页数 353
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 机械、仪表工业;
  • 关键词

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