Denoising and chaotic feature extraction of acoustic emission signals of hydraulic turbine cavitation

被引：0
|
作者：
Liu Z. [1 ]
Li X. [1 ]
Zou S. [1 ]
Wang W. [1 ]
Zhou Z. [1 ]
机构：
[1] School of Energy and Power Engineering, Changsha University of Science and Technology, Changsha
来源：
Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University | 2023年 / 44卷 / 08期
关键词：
acoustic emission; cavitation; chaotic characteristic; Fourier decomposition method; hydraulic turbine; multiresolution singular value decomposition; phase space reconstruction;
D O I：
10.11990/jheu.202207030
中图分类号：
学科分类号：
摘要：
This study aims to address the problem concerning the influence of noise on the effective extraction of hydraulic turbine cavitation acoustic emission signal characteristics. Therefore, a processing method for the turbine cavitation acoustic emission signal is established based on the Fourier decomposition method and multiresolution singular value decomposition FDM-MRSVD denoising and chaos feature extraction. First, the cavitation acoustic emission signal is decomposed into several Fourier intrinsic band functions (FIBFs) of instantaneous frequencies based on FDM, and the correlation coefficients are calculated. The FIBFs with a small correlation coefficient are denoised by using MRSVD, and the denoised FIBFs are reconstructed with a large correlation coefficient FIBFs to complete signal denoising. The phase space is reconstructed, obtaining the phase locus and Poincaré section as the signal characteristics. The experimental results show that the denoising method of FDM-MRSVD can achieve superior noise reduction by cavitation acoustic emission of hydraulic turbines. The chaotic characteristic images can represent the change rule of the cavitation state. © 2023 Editorial Board of Journal of Harbin Engineering. All rights reserved.
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页码：1361 / 1367
页数：6
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