基于MIGA-VMD和t-SNE的轴承故障诊断方法北大核心

针对从汽轮机轴承的非线性、非平稳振动信号中提取故障特征困难而导致诊断识别率低的问题,提出一种基于MIGA-VMD和排列熵、t-SNE的特征提取方法。变分模态分解(VMD)在轴承故障诊断中的分解效果很大程度上取决于分解个数和惩罚参数的选取。为实现VMD相关参数的最优选择,采用多岛遗传算法(MIGA)对VMD参数进行优化。利用参数优化的VMD将轴承原始振动信号分解为若干本征模态分量,计算与原始信号相关性较高的部分模态分量的排列熵构成故障特征,利用t-SNE方法进行降维得到低维特征向量并将其作为支持向量机分类器的输入,实现故障类型的诊断。将该方法应用到轴承故障诊断中并与EMD+排列熵+t-SNE、EEMD+排列熵+t-SNE、LMD+排列熵+t-SNE、传统VMD+排列熵+t-SNE四种特征提取方法进行对比。实验结果表明:该方法能更准确地提取轴承的故障特征,有效实现轴承的故障诊断。

Bearing Fault Diagnosis Method Based on MIGA Optimized VMD Parameters and t-SNE

Aiming at the problem that it is difficult to extract fault features from the nonlinear and non-stationary vibration signals of steam turbine bearings, which results in low diagnosis and recognition rate, a feature extraction method based on MIGA-VMD decomposition and t-SNE was proposed. The decomposition effect of variational mode decomposition (VMD) in bearing fault diagnosis largely depends on the number of decompositions and the selection of penalty parameters. In order to achieve the optimal selection of VMD related parameters, multiple-island genetic algorithm was used to optimize the VMD parameters. The parameter-optimized VMD was used to decompose the original vibration signal of the bearing into a number of intrinsic modal functions, and the permutation entropy of the partial modal components with higher correlation with the original signal was calculated to form the fault feature.t-SNE method was used to reduce the dimension to obtain low-dimensional feature vector and it was input to support vector machine classifier to realize fault type diagnosis. This method was applied to bearing fault diagnosis and compared with four feature extraction methods: EMD+ permutation entropy+t-SNE, EEMD+permutation entropy+t-SNE, LMD+permutation entropy+t-SNE, traditional VMD+permutation entropy+t-SNE. Experimental results show that this method can be used to more accurately extract the fault characteristics of the bearing, and to effectively realize the bearing fault diagnosis.