基于NLM-CEEMDAN和样本熵的水电机组振动信号去噪

Vibration Signal De-noising of Hydropower Units Based on NLM-CEEMDAN and Sample Entropy

振动监测分析是水电机组故障诊断的重要手段,如何从振动信号中滤除噪声以便于故障特征有效提取是关键问题,为此提出了基于非局部均值滤波(NLM)和自适应噪声完备集合经验模态分解方法(CEEMDAN)结合的振动信号去噪新方法,并在水电机组摆度监测分析中进行了应用。该方法利用NLM-CEEMDAN对信号进行降噪处理,获得若干个固有模态分量(IMF),并且计算各分量样本熵值来进行分量归类。最后通过将高频噪声分量和信噪混合分量中的噪声成分从原始信号中滤除来完成对振动信号的去噪。仿真和实例分析,该方法优于常用的分解分量重构法和小波去噪算法,具有更好的去噪效果,为水电机组故障特征提取提供了新思路。

Vibration monitoring and analysis are an important means of fault diagnosis of hydropower units.How to filter noise from vibration signals to extract fault features effectively is a key problem.Therefore,a new method for the analysis of throw signals is proposed based on non-local means(NLM)and complementary ensemble empirical mode decomposition with adaptive noise(CEEMDAN)in this paper,which is applied in the swing monitoring and analysis of hydropower units.In this method,the signals are denoised by NLM-CEEMDAN.Several intrinsic mode function(IMF)are obtained and the sample entropy of each component is calculated for component classification.The denoising of the throw signal is completed by discarding the high-frequency noise components and noise component of the mixed information and noise components.Through an analysis of simulated signals and real-world signals,it can be found that this method is superior to the conventional decomposition component reconstruction and wavelet threshold denoising algorithms,which can remove noise more effectively,providing a new idea for the fault feature extraction of hydropower units.