Volume 40, Number 4, August 2022
|Page(s)||771 - 777|
|Published online||30 September 2022|
Exploring mono-fractal characteristics of dynamic pressure at exit of centrifugal compressor based on EWT
School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China
2 State Key Laboratory of Compressor Technology, Hefei General Machinery Research Institute Co., Ltd, Hefei 230031, China
This paper studies the dynamic pressure at the outlet of the 800 kW centrifugal compressor. It uses the single-fractal method and the empirical wavelet transform to analyze the nonlinear characteristics of the centrifugal compressor under different working conditions. First, it analyzes the characteristics of the dynamic pressure waveform when the centrifugal compressor enters the surge state from the steady state through the transition process. It also uses the empirical wavelet transform to extract the modal components of the pressure waveform, which is reconstructed according to the correlation coefficient. Second, it studies the characteristics of the autocorrelation function of the reconstructed signal under different numbers of hysteresis points k. Finally, it studies the Hurst parameters of the original signal and the reconstructed signal to identify the working state of the centrifugal compressor. The results show that the autocorrelation function of the reconstructed signal can effectively reflect the working state of the centrifugal compressor. Compared with the original signal, it is easier for the Hurst parameter of the reconstructed signal to identify the surge state of the centrifugal compressor.
以800 kW离心压缩机的出口动态压力为研究对象, 采用经验小波变换并结合单重分形, 分析系统在不同工况下的非线性特征。分析系统从稳定状态经过过渡过程进入喘振状态的出口动态压力波形特征, 采用经验小波变换提取波形的模态分量并根据相关系数进行波形重构; 研究重构信号在不同迟滞点数k下的自相关函数特征; 对比分析原始信号及重构信号的Hurst参数, 以识别系统的工作状态。研究结果显示: 重构信号的自相关函数可以有效地反映系统的工作状态。此外, 相对于原始信号, 重构信号的Hurst参数更易识别出系统的喘振状态。
Key words: centrifugal compressor / surge state / empirical wavelet transform / mono-fractal characteristic / autocorrelation function / Hurst parameter
关键字 : 离心压缩机 / 喘振 / 经验小波变换 / 单重分形 / 自相关函数 / Hurst参数
© 2022 Journal of Northwestern Polytechnical University. All rights reserved.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.