基于优化VMD组合降噪和LMD的水轮机空化声发射信号特征提取

Feature extraction of cavitation acoustic emission signal of hydraulic turbine based on optimized VMD combined noise reduction and LMD

针对水轮机空化声发射信号存在噪声,进而影响信号特征有效提取的问题,提出基于优化变分模态分解(VMD)与Birge-Massart策略组合降噪和局部均值分解(LMD)的水轮机空化声发射信号特征提取方法.针对VMD算法中惩罚因子和分解模态数对分解结果有着显著影响,提出以散布熵差异相关系数最小值为目标函数,利用哈里斯鹰优化算法(HHO)对VMD进行参数寻优.以最优参数的VMD分解信号,得到一系列本征模态函数(IMF).计算各IMF的相关系数,对相关系数小于0.1的IMF进行剔除,大于0.5的IMF进行保留,0.1到0.5的IMF采用小波BM准则进行降噪,并与保留的分量重构.对重构信号进行LMD处理,将分解得到的乘积函数(PF)分量的能量提取为信号特征.试验分析结果表明,经过优化VMD组合降噪处理和LMD处理得到PF分量的能量与空化系数之间呈负相关,验证了所提方法用于水轮机空化状态识别的可行性.

Aiming at the problem that noise exists in the acoustic emission signal of hydraulic turbine cavitation,which affects the effective extraction of signal features,a feature extraction method of acoustic emission signal of hydraulic turbine cavitation based on the combination of noise reduction and local mean decomposition(LMD)of optimized variational modal decomposition(VMD)and Birge-Massart strategy was proposed.In view of the significant influence of penalty factor and decomposition mode number on the decomposition results in VMD algorithm,the minimum value of dispersion entropy difference correlation coefficient was proposed as the objective function,and Harris Hawk Optimization(HHO)was used to optimize the parameters of VMD.The signal was decomposed with the VMD of the optimal parameters,and a series of Intrinsic Mode Functions(IMF)were obtained.The correlation coefficient of each IMF is calculated,the IMF with correlation coefficient less than 0.1 were eliminated,the IMF greater than 0.5 were retained,while the IMF between 0.1 and 0.5 were denoised using the wavelet BM criterion,and reconstructed with the retained components.The reconstructed signal was processed by LMD,and the energy of the decomposed Product Function(PF)component was extracted as the signal feature.The experimental results show that there is a negative correlation between PF component energy and cavitation coefficient after optimized VMD combined noise reduction treatment and LMD treatment,which verifies the feasibility of the proposed method for cavitation state identification of hydraulic turbines.