基于EEMD近似熵的水电机组振动信号特征提取

Vibration feature extraction for hydropower units based on ensemble empirical mode decomposition and approximate entropy

对于水电机组非平稳非线性振动信号特征提取方法的研究近年来一直是水电机组故障诊断领域研究热点,特征提取的有效性直接关系到故障诊断的准确性。本文提出基于集合经验模态分解(EEMD)和近似熵的水电机组振动信号特征提取方法,将信号经EEMD分解后筛选得到的本征模态分量(IMF)近似熵特征值输入概率神经网络(PNN)进行模式识别。采用经验模态分解(EMD)和近似熵特征提取方法进行对比实验。识别结果表明:采用EEMD和近似熵的特征提取方法,能有效区分机组不同的运行状态,可为实际工程应用提供理论依据。

In recent years, research on the feature extraction method of non-stationary nonlinear vibration signals has been a hot spot in the fault diagnosis of hydropower units, and its effectiveness is a key factor of fault diagnosis accuracy. This paper describes a new feature extraction method based on ensemble empirical mode decomposition(EEMD) and approximate entropy for vibration signals from hydropower units. It uses EEMD to obtain approximate entropy eigenvalues of the intrinsic mode function(IMF), and then inputs them into a probability neural network(PNN) for pattern identification. Results show this method can effectively distinguish the difference in the operation modes of a unit, laying a basis for fault diagnosis in engineering applications.