噪声与振动控制2025，Vol.45Issue(2)：70-75,111,7.DOI:10.3969/j.issn.1006-1355.2025.02.012
基于改进HHO的水轮机空化信号降噪及特征提取
Noise Reduction and Feature Extraction of Signals Induced by Hydraulic Turbine Cavitation Based on Improved HHO
摘要
Abstract
In order to reduce the noise and extract time-frequency features of acoustic emission signals induced by hy-draulic turbine cavitation,a denoising and feature extraction method based on Improved Harris Hawks Optimization algo-rithm(IHHO)and Fluctuation-Based Dispersion Entropy(FDE)was proposed.Firstly,the global search stage of the Harris Hawks Optimization algorithm(HHO)was improved by utilizing the spiral search mechanism of the Bald Eagle Search algo-rithm(BES).Then,with the dispersion entropy difference correlation coefficient as the objective function,IHHO was used to find the optimal parameters of VMD.The optimal parameters of VMD were used to decompose the signal,and then the signal was reconstructed based on the correlation coefficient threshold to achieve noise reduction.Finally,the energy and fluctuation-based dispersion entropy features of the acoustic emission signal were extracted,and the relationship between the variation of cavitation coefficient and the features was analyzed.The results show that compared to the Grey Wolf-Cuckoo algorithm(GWO-CS)and HHO,the IHHO has a better denoising effect on optimizing VMD parameters.The energy of the acoustic emission signal increases first and subsequently decreases as the cavitation coefficient decreases,and with continu-ous decreasing of the cavitation coefficient,the energy of sound emission signals fluctuates.The fluctuation-based dispersion entropy shows a trend of decreasing first and then increasing when the cavitation coefficient decreases.关键词
声学/水轮机/空化/声发射/降噪/哈里斯鹰优化算法/秃鹰搜索算法Key words
acoustics/hydraulic turbine/cavitation/acoustic emission/noise reduction/Harris hawks optimization al-gorithm/bald eagle search algorithm分类
能源科技引用本文复制引用
刘忠,刘圳,邹淑云,周泽华,乔帅程..基于改进HHO的水轮机空化信号降噪及特征提取[J].噪声与振动控制,2025,45(2):70-75,111,7.基金项目
国家自然科学基金资助项目(52079011) （52079011）
湖南省自然科学基金资助项目(2023JJ30032) （2023JJ30032）
