基于层次散布熵的滚动轴承剩余寿命预测方法

针对滚动轴承寿命预测中提取的特征不准确以及预测精度低等问题，提出一种基于层次散布熵(HDE)和门控循环单元(GRU)的滚动轴承剩余寿命预测方法。首先将振动信号时间序列进行层次分析，计算各个节点的散布熵，将散布熵重构融合得到HDE;其次将相关性、单调性和鲁棒性组合形成综合指标，用来验证HDE的优越性；最后划分训练集和测试集，通过GRU网络进行寿命预测试验。结果表明，HDE的综合指标值最优，所提方法HDE-GRU的预测误差比RMS-GRU、DE-GRU和MDE-GRU分别低42.77%、39.57%和20.24%,且运行时间最短，预测精度更高，为滚动轴承健康管理提供了实际价值。

Remaining life prediction of rolling bearings based on hierarchical dispersion entropy

Aiming at the problems of inaccurate features extracted and low prediction accuracy in the life prediction of rolling bearings,a method of remaining life prediction of rolling bearings based on hierarchical dispersion entropy (HDE) and gated recurrent unit (GRU) was proposed.Firstly,the time series of vibration signals were analyzed by hierarchical analysis,and the dispersion entropy of each node was calculated,and the dispersion entropy was reconstructed and fused to obtain the HDE.Secondly,correlation,monotonicity and robustness are combined to form a comprehensive index to verify the superiority of HDE.Finally,the training set and test set of HDE are divided,and the life prediction test is carried out by GRU network.The results show that the comprehensive index value of HDE is the best,and the prediction error of the proposed method HDE-GRU is 42.77%,39.57% and 20.24% lower than that of RMS-GRU, DE-GRU and MDE-GRU,respectively.It has the shortest running time and higher prediction accuracy,which provides practical value for rolling bearing health management.