基于MCKD-EEMD近似熵和TWSVM的齿轮箱故障诊断

在复杂环境下齿轮箱信号往往会淹没在噪声信号中,特征向量难以提取；为了有效地进行故障诊断,提出了基于最大相关反褶积(MCKD)总体平均经验模态分解(EEMD)近似熵和双子支持向量机(TWSVM)的齿轮箱故障诊断方法;首先采用MCKD方法对强噪声信号进行滤波处理,在采用EEMD方法对齿轮箱信号进行分解,分解后得到本征模函数(IMF)分量进行近似熵求解,得到齿轮特征向量,最后将其输入到TWSVM分类器中进行故障识别;仿真实验表明,采用MCKD-EEMD方法能够有效地提取原始信号,与其他分类器相比,TWSVM的计算时间短,分类效果好等优点。

Gearbox Fault Diagnosis Based on MCKD-EEMD-Approximate Entropy and TWSVM

Gearbox signals always tend to be drowned in the noise at a complex environment, resulting problem in feature vector extraction/which make difficulty for feature vector extraction. In order to effectively diagnose gearbox fault, a method for gearbox is proposed based on maximum correlated kurtosis deconvolution (MCKD) ensemble empirical mode decomposition (EEMD) approximate entropy and twin support vector machine (TWSVM). Firstly, the noise signal is filtered by MCKD method, then EEMD method is used to decompose the signal of gearbox. To get signal eigenvectors, the intrinsic mode functions (IMF) obtained from the decomposition is used to calculate approximate entropy. Finally, signal eigenvectors are input to the TWSVM classifier for fault identification. Simulation results show that the MCKD-EEMD method can be used to extract raw signal effectively, and the TWSVM classifier performs a better classification.