Open Access
 Issue JNWPU Volume 40, Number 4, August 2022 771 - 777 https://doi.org/10.1051/jnwpu/20224040771 30 September 2022

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.

1 相关理论

1.1 经验小波分解

 图1EWT分解图

1.2 单重分形及其物理意义

1.2.2 Hurst参数及R/S算法

Hurst参数可以量化时间序列的自相关性, 从而衡量其长期记忆性。通常采用R/S分析法(又称为重标极差分析法)根据时间序列按照长相关特性的幂指数规律进行计算[9]。

xk的累积偏差为Xt, N, 则

xk的标准差去除R, 进行重标, 可得

2 出口动态压力采集及波形分析

2.2 离心压缩机出口处动态压力波形

 图2离心压缩机出口动态压力波形(压力计算单位为PSI)

3 出口处动态压力波形的重构

 图3出口处动态压力的EWT分解结果及重构信号

4 出口处动态压力波形的单重分形

4.1 自相关函数

 图4不同状态下的自相关函数(k=40 960)
 图5不同状态下的自相关函数(k=50)

4.2 Hurst参数

 图6Hurst参数对比

References

1. GUNADAL S M, GOVARDHAN M. Improvement in stable operating range of a centrifugal compressor with leaned diffuser vanes[J]. Journal of Mechanical Science and Technology, 2019, 33(11): 5261–5269 [Article] [CrossRef] [Google Scholar]
2. LIU Yan, GAO Kuan, HE Hao, et al. Nonlinear characteristics of centrifugal compressor outlet dynamic pressure based on multifractal and their application in surge identification[J]. Journal of Vibration and shock, 2021, 40(1): 205–211 [Article] (in Chinese) [Google Scholar]
3. LIU Yan, DING Dongxiao, MA Kai, et al. Descriptions of entropy with fractal dynamics and their applications to the flow pressure of centrifugal compressor[J]. Entropy, 2019, 21(3): 266[Article] [NASA ADS] [CrossRef] [Google Scholar]
4. LIU Yan, HE Hao, XIAO Jun. Correlation dimension characteristics analysis of dynamic pressure at centrifugal compressor outlet[J]. Journal of Aerospace Power, 2021, 36(2): 300–309 [Article] (in Chinese) [Google Scholar]
5. LIU Yan, CHEN Dangmin, LIU Liguang, et al. Exploring monofractal characteristics of dynamic pressure at exit of centrifugal compressor[J]. Journal of Northwestern Polytechnical University, 2013, 31(1): 60–66 [Article] (in Chinese) [Google Scholar]
6. GILIS J. Empirical waveletransform[J]. IEEE Trans on Signal Processing, 2013, 61(16): 3999–4010 [Article] [CrossRef] [Google Scholar]
7. OUNG Q W, MUTHUSAMY H, BASAH S N. et al. Empirical wavelet transform based features for classi fication of parkinson's disease severitu[L]. Journal of Medlical Systems, 2018, 42(2): 292-308 [Google Scholar]
8. KEDADOUCHE M, THOMAS M, TAHAN A. A comparative studly between empirical wavelet transforms and empirical mode decomposition methods: application to bearing defect diagnosis[J]. Mechanical Systems and Signal Processing, 2016, 81: 88–107 [Article] [CrossRef] [Google Scholar]
9. ZHANG Jizhong. Fractal[M]. Beijing: Tsinghua University Press, 1995 (in Chinese) [Google Scholar]

All Figures

 图1EWT分解图 In the text
 图2离心压缩机出口动态压力波形(压力计算单位为PSI) In the text
 图3出口处动态压力的EWT分解结果及重构信号 In the text
 图4不同状态下的自相关函数(k=40 960) In the text
 图5不同状态下的自相关函数(k=50) In the text
 图6Hurst参数对比 In the text

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.