Tu Deyu, Zheng Jinde, Jiang Zhanwei, Pan Haiyang
School of Mechanical Engineering, Anhui University of Technology, Maanshan 243032, China.
Entropy (Basel). 2018 May 11;20(5):360. doi: 10.3390/e20050360.
As a nonlinear dynamic method for complexity measurement of time series, multiscale entropy (MSE) has been successfully applied to fault diagnosis of rolling bearings. However, the MSE algorithm is sensitive to the predetermined parameters and depends heavily on the length of the time series and MSE may yield an inaccurate estimation of entropy or undefined entropy when the length of time series is too short. To improve the robustness of complexity measurement for short time series, a novel nonlinear parameter named multiscale distribution entropy (MDE) was proposed and employed to extract the nonlinear complexity features from vibration signals of rolling bearing in this paper. Combining with t-distributed stochastic neighbor embedding (t-SNE) for feature dimension reduction and Kriging-variable predictive models based class discrimination (KVPMCD) for automatic identification, a new intelligent fault diagnosis method for rolling bearings was proposed. Finally, the proposed approach was applied to analyze the experimental data of rolling bearings and the results indicated that the proposed method could distinguish the different fault categories of rolling bearings effectively.
作为一种用于时间序列复杂性测量的非线性动力学方法，多尺度熵（MSE）已成功应用于滚动轴承的故障诊断。然而，MSE算法对预定参数敏感，并且严重依赖于时间序列的长度，当时间序列长度过短时，MSE可能会产生不准确的熵估计或未定义的熵。为了提高短时间序列复杂性测量的鲁棒性，本文提出了一种名为多尺度分布熵（MDE）的新型非线性参数，并用于从滚动轴承振动信号中提取非线性复杂性特征。结合用于特征降维的t分布随机邻域嵌入（t-SNE）和用于自动识别的基于克里金变量预测模型的类判别（KVPMCD），提出了一种新的滚动轴承智能故障诊断方法。最后，将所提出的方法应用于分析滚动轴承的实验数据，结果表明该方法能够有效地区分滚动轴承的不同故障类别。
