基于改进EMD与关联维数的水轮机空化声发射信号特征提取
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摘要
为研究水轮机空化状态下声发射信号的特征及其演变规律,提出了基于改进经验模态分解(Empirical Mode Decomposition, EMD)与关联维数的水轮机空化声发射信号特征提取方法。采用镜像延拓与可变余弦窗函数相结合的方法对传统EMD进行改进。将改进EMD方法应用于混流式水轮机模型空化状态下的声发射信号分析中,分别采用自相关法和假近邻法计算声发射信号各阶固有模态函数(Intrinsic Mode Function, IMF)分量的时间延迟参数和最佳嵌入维数,采用G-P算法提取各阶IMF的关联维数,分析关联维数随水轮机空化系数的变化关系。结果表明:随着空化状态从无空化、初生空化到临界空化,声发射信号的各阶IMF关联维数逐渐增大,直接反映了水轮机空化从无到有,从弱到强,水流流态更加复杂和紊乱的过程。
To investigate the characteristics and evolution law of acoustic emission signals, a feature extraction method was proposed for the acoustic emission signals under hydraulic turbine cavitation conditions based on improved Empirical Mode Decomposition(EMD) and correlation dimension. The method of mirror extension combined with variable cosine window function was used to improve the conventional EMD, and the improved EMD was then applied to analyze the acoustic emission signals under cavitation conditions of a Francis hydraulic turbine. The time delay parameters and the best embedding dimensions for each order of intrinsic mode function(IMF) component of the acoustic emission signals were subsequently calculated respectively by autocorrelation method and pseudo-nearest-neighbor analysis algorithm, while the correlation dimensions of each order IMF were extracted using G-P algorithm, so as to study the change rules of the correlation dimension with the cavitation coefficient of the Francis turbine. Results show that with the cavitation state changing from non-cavitation to incipient cavitation and further to critical cavitation, the correlation dimension of each order of IMF components gradually increases, which directly reflects the cavitation evolution of the hydraulic turbine growing out of nothing from weak to strong, where the flow state becomes more turbulent.
To investigate the characteristics and evolution law of acoustic emission signals, a feature extraction method was proposed for the acoustic emission signals under hydraulic turbine cavitation conditions based on improved Empirical Mode Decomposition(EMD) and correlation dimension. The method of mirror extension combined with variable cosine window function was used to improve the conventional EMD, and the improved EMD was then applied to analyze the acoustic emission signals under cavitation conditions of a Francis hydraulic turbine. The time delay parameters and the best embedding dimensions for each order of intrinsic mode function(IMF) component of the acoustic emission signals were subsequently calculated respectively by autocorrelation method and pseudo-nearest-neighbor analysis algorithm, while the correlation dimensions of each order IMF were extracted using G-P algorithm, so as to study the change rules of the correlation dimension with the cavitation coefficient of the Francis turbine. Results show that with the cavitation state changing from non-cavitation to incipient cavitation and further to critical cavitation, the correlation dimension of each order of IMF components gradually increases, which directly reflects the cavitation evolution of the hydraulic turbine growing out of nothing from weak to strong, where the flow state becomes more turbulent.
引文
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[2] DEZHKUNOV N V,FRANCESCUTTO A,SERPE L,et al.Sonoluminescence and acoustic emission spectra at different stages of cavitation zone development[J].Ultrasonics Sonochemistry,2018,40:104-109.
[3] 李静,周建中,肖剑,等.基于小波包变换和关联维数的空化信号特征提取[J].水力发电,2013,39(10):53-57.LI Jing,ZHOU Jianzhong,XIAO Jian,et al.Feature extraction of turbine cavitation based on wavelet packet and fractal analysis[J].Water Power,2013,39(10):53-57.
[4] 刘忠,邹淑云,陈莹,等.水轮机空化状态声发射信号的小波包能量特征[J].水力发电学报,2018,37(1):87-93.LIU Zhong,ZOU Shuyun,CHEN Ying,et al.Wavelet packet energy features of acoustic emission signals from hydraulic turbines under cavitation[J].Journal of Hydroelectric Engineering,2018,37(1):87-93.
[5] LEI Yaguo,LIN Jing,HE Zhengjia,et al.A review on empirical mode decomposition in fault diagnosis of rotating machinery[J].Mechanical Systems and Signal Processing,2013,35(1/2):108-126.
[6] SIRACUSANO G,LAMONACA F,TOMASELLO R,et al.A framework for the damage evaluation of acoustic emission signals through Hilbert-Huang transform[J].Mechanical Systems and Signal Processing,2016,75:109-122.
[7] 薛延刚,王瀚.基于HHT的水轮机空化信号研究[J].水力发电学报,2015,34(5):147-151.XUE Yan'gang,WANG Han.Investigation on turbine cavitation signals analysis based on Hilbert-Huang transform[J].Journal of Hydroelectric Engineering,2015,34(5):147-151.
[8] 沈路,周晓军,张志刚,等.Hilbert-Huang变换中的一种端点延拓方法[J].振动与冲击,2009,28(8):168-171,174.SHEN Lu,ZHOU Xiaojun,ZHANG Zhigang,et al.Boundary-extension method in Hilbert-Huang transform[J].Journal of Vibration and Shock,2009,28(8):168-171,174.
[9] 徐力彬,宋余庆,刘毅.基于镜像延拓和窗函数的端点效应抑制方法[J].计算机工程,2015,41(4):112-116.XU Libin,SONG Yuqing,LIU Yi.Restraining method for end effect based on mirror extension and window function[J].Computer Engineering,2015,41(4):112-116.
[10] LI Jinxia,WANG Chao,DING Hongbing,et al.EMD and spectrum-centrobaric-correction-based analysis of vortex street characteristics in mist annular flow of wet gas[J].IEEE Transactions on Instrumentation and Measurement,2018,67(5):1150-1160.
[11] 祁艳杰,王黎明,杨泽辉,等.几种改善EMD端点效应方法的比较研究[J].现代电子技术,2013,36(22):50-52,56.QI Yanjie,WANG Liming,YANG Zehui,et al.Study on methods for improving EMD end effect[J].Modern Electronics Technique,2013,36(22):50-52,56.
[12] DLASK M,KUKAL J.Application of rotational spectrum for correlation dimension estimation[J].Chaos,Solitons & Fractals,2017,99:256-262.
[13] 卢绪祥,苏一鸣,吴家腾,等.基于EMD及灰色关联度的滑动轴承润滑状态故障诊断研究[J].动力工程学报,2016,36(1):42-47.LU Xuxiang,SU Yiming,WU Jiateng,et al.Fault diagnosis on lubrication state of journal bearings based on EMD and grey relational degree[J].Journal of Chinese Society of Power Engineering,2016,36(1):42-47.
[14] 刘忠,邹淑云,陈莹,等.混流式水轮机模型空化状态与声发射信号特征关系试验[J].动力工程学报,2016,36(12):1017-1022.LIU Zhong,ZOU Shuyun,CHEN Ying,et al.Experiments on the relationship between cavitation status and acoustic emission signal features for a Francis turbine model[J].Journal of Chinese Society of Power Engineering,2016,36(12):1017-1022.