基于经验小波变换和关联维数的风力机齿轮箱故障诊断

Fault Diagnosis of a Wind Turbine Gearbox Based on Empirical Wavelet Transform and Correlation Dimension

针对齿轮箱故障信号的非线性和非平稳性特征,提出基于经验小波变换(Empirical WaveletTransform,EWT)、关联维数(Correlation Dimension,CD)和支持向量机(Support Vector Machine,SVM)的故障诊断方法。首先通过EWT对风力机齿轮箱信号进行分解,得到若干本征模态函数(Intrinsic Mode Function,IMF)分量,再采用G-P算法求取各组IMF分量的关联维数,并将各组关联维数特征集输入SVM中完成故障识别及分类。结果表明:振动信号关联维数与嵌入维数呈正相关,且正常信号与故障信号的关联维数区分度不明显,通过SVM能对其进行精确识别和分类;该方法能有效提取系统故障非线性特征,故障识别准确率高达100%。

Aiming at the nonlinear and instable characteristics of gearbox fault signals, a method was proposed based on empirical wavelet transform(EWT), correlation dimension(CD) and support vector machine(SVM) for fault diagnosis of the wind turbine gearbox. Firstly, EWT was used to decompose the vibration signal into several intrinsic mode functions(IMFs). Then, G-P algorithm was adopted to obtain the correlation dimensions of each group of IMFs. Finally, the correlation dimensions were taken as the input parameters of the SVM to diagnose the fault. Results show that the correlation dimension of the vibration signal is positively correlated with the embedding dimension. The correlation dimension of a normal signal is slightly different from that of a fault signal, which could be accurately identified and classified by the SVM, proving the method proposed to be effective in extracting the nonlinear characteristics of the fault, with a diagnostic accuracy up to 100%.