基于改进CEEMDAN和t-SNE的故障特征提取方法

针对非线性、非稳定振动信号难以提取有效故障特征的问题，提出一种基于改进自适应噪声完备集合经验模态分解（CEEMDAN）和t-分布随机邻域嵌入（t-SNE）算法相结合的故障特征提取方法。利用三次Hermite插值代替三次样条插值构造包络线，提高传统CEEMDAN对非平稳信号的分解精度；利用改进后的CEEMDAN对原始信号分解并通过相关系数筛选出有效固有模态分量（IMF），提取有效IMF分量的时频特征、奇异值和能量值构建高维混合域特征集；最后，通过t-SNE算法挖掘高维混合域特征信息得到低维敏感特征，并将其输入到支持向量机中进行分类，以分类准确率作为特征提取效果评价指标。在齿轮箱故障模拟实验台进行实验验证，结果表明该方法能够准确地提取故障特征，为故障特征提取提供新思路。

Fault Feature Extraction Method Based on Improved CEEMDAN and t-SNE

To solve the problem that it is difficult to extract effective fault features from nonlinear and unstable vibration signals,a fault feature extraction method was proposed based on improved adaptive noise complete set empirical mode decomposition (CEEMDAN) and t-distributed stochastic neighbor embedding (t-SNE) algorithm.Cubic Hermite interpolation was used to replace cubic spline interpolation to construct envelope,which could improve the decomposition accuracy of non-stationary signal by traditional CEEMDAN.The original signal was decomposed by the improved CEEMDAN,and the effective intrinsic mode components (IMF) were screened by correlation coefficients,and the time-frequency features,singular values and energy values of the effective IMF components were extracted to construct a high-dimensional mixed domain feature set.Finally,t-SNE algorithm was used to mine high-dimensional mixed domain feature information to obtain low-dimensional sensitive features,which were input into support vector machine for classification,and the classification accuracy rate was used as the evaluation index of feature extraction effect.The experimental verification on the gearbox fault simulation test bed shows that this method can accurately extract fault features,and provide a new idea for fault feature extraction.