Surge Monitoring Method Based on Nonlinear Manifold Learning

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Surge Monitoring Method Based on Nonlinear Manifold Learning

更新时间：2025-07-09

- Surge Monitoring Method Based on Nonlinear Manifold Learning
- Vol. 43, Issue 7, Pages: 44-48(2009)
- 作者机构：
  
  西安交通大学机械工程学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP206.3
- Online First：10 July 2009，
  
  Published：2009
- 稿件说明：
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Surge Monitoring Method Based on Nonlinear Manifold Learning[J]. 2009, 43(7): 44-48.
DOI：

Surge Monitoring Method Based on Nonlinear Manifold Learning[J]. 2009, 43(7): 44-48. DOI：

摘要

为了提取压缩机喘振发作时表现出的非线性特性

引入了一种新的喘振特征提取方法.首先对原始信号进行多元统计分析

构造高维特征空间

然后利用局部切空间排列的流形学习方法提取出一维主流形

进而通过主流形几何结构的变化来反映系统的非线性变化.分析结果表明

与相关积分方法相比

该方法可以提前1 s识别出喘振特征

并且能够降低误报率

因此在喘振监测中具有良好的应用前景.

Abstract

To extract the nonlinear characteristic of compressor surge

a new monitoring method based on manifold learning is proposed. High dimension space of raw pressure signals is constructed by multivariate statistical analysis

then the local target space alignment(LTSA)algorithm is employed for extracting one dimension principal manifold

to reflect the non-linear dynamic characteristics of compressor system. Experimental results and industrial measurements show that this approach

compared with the correlation integral method

forecasts surge about 1s in advance with lower false prediction rate.

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Keywords

references

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