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基于EMD-样本熵的轴流压气机喘振分析
Surge analysis of axial compressor based on EMD - sample entropy
- DOI:
- 作者:
- 黄泽浩, 李良才, 邵勇, 张凡, 胡肖肖
HUANG Ze-hao, LI Liang-cai, SHAO Yong, ZHANG Fan, HU Xiao-xiao
- 作者单位:
- 中国舰船研究设计中心, 湖北 武汉 430064
China Ship Development and Design Center, Wuhan 430064, China
- 关键词:
- 轴流压气机;喘振;EMD;数据降维;样本熵
axial flow compressor; surge; EMD; data dimension reduction; the sample entropy
- 摘要:
- 轴流压气机作为燃气轮机安全运行的重要设备装置，它的稳定性直接影响燃气轮机的输出效率。因此如何快速准确判断并及时规避轴流压气机的喘振工况是燃气轮机优化设计的重点。本文提出一种基于轴流压气机已有传感器处理提取特征值后对喘振工况进行判断的新方法。首先对压气机的一维传感信号进行EMD分解升维后进行数据降维至数据本质维数上，再利用样本熵处理后提取特征信号从而对喘振工况进行判定。基于三级轴流压气机喘振进行喘振的诱导试验，实现压气机的喘振工况并且利用EMD-样本熵的方法对压气机的级间压力、出口压力进行分析对比。试验结果表明，此方法提取的特征值可以有效区分出压气机正常与喘振的工况，验证了方法的可行性，为轴流轴流机的故障预测提供基础。
Axial flow compressor is an important equipment for safe operation of gas turbine, and its stability directly affects the output efficiency of gas turbine. Therefore, how to quickly and accurately judge and timely avoid the surge condition of axial flow compressor is the key point of gas turbine optimization design. This paper preposes a new method to judge the surge condition based on the feature values extracted from the existing sensors of axial flow compressor. Firstly, the one-dimensional sensing signal of compressor is decomposed by EMD and dimensionality is increased, then the data dimensionality is reduced to the essential dimension of the data, and then the characteristic signal is extracted after sample entropy processing to determine the surge condition. Based on the three-stage axial compressor surge surge of induction experiment was carried out, the successful implementation of the compressor surge condition and using the EMD - data dimension reduction - sample entropy method of compressor interstage pressure, outlet pressure are analyzed and compared, the test results show that this method is to extract the characteristic value can effectively distinguish between normal and surge of compressor working condition. The feasibility of the method is verified, and it provides a basis for fault prediction of axial flow machine.
2023,45(9): 116-119 收稿日期：2022-11-04
DOI：10.3404/j.issn.1672-7649.2023.09.025
分类号：U661.44
作者简介：黄泽浩(1997-),男,硕士,工程师,研究方向为舰船动力系统
