1.Tiangong University;2.Tianjin Chengjian University
为了提高滚动轴承剩余寿命的预测精度，提出了一种变分模态分解（variational modal decomposition，VMD）和门控循环神经网络（gated recurrent neural networks，GRU）融合算法的滚动轴承剩余寿命预测模型VMD-GRU。该模型首先通过麻雀搜索算法（sparrow search algorithm，SSA）优化的VMD算法对原始振动信号进行模态分解，然后利用峭度准则选择有效模态分量进行退化特征提取；其次进行随机森林算法特征重要性分析，并构建退化特征决策表；为了保证模型准确率，最后通过算数优化算法（arithmetic optimization algorithm，AOA）优化GRU中的超参数，并根据不同故障类型建立GRU剩余寿命预测模型。使用XJTU-SY标准数据集进行剩余寿命预测验证，与传统未结合故障类型提取退化特征和建立预测模型方法相比，实验结果表明，VMD-GRU模型均方根误差和平均绝对误差分别减少21.26%和40.15%。
In order to improve the prediction accuracy of the residual lifetime of rolling bearings, a new model for predicting the residual lifetime of rolling bearings based on the fusion algorithm of variation modal decomposition (VMD) and gated recurrent neural networks (GRU) was proposed. Firstly, the original vibration signal was decomposed by the VMD algorithm which is optimized by Sparrow Search algorithm (SSA). Secondly, the effective modal components were selected by kurtosis criterion to extract the degenerate features. And then, the importance of the features of the stochastic forest algorithm was analyzed, and the decision table of the degradation features was constructed. In order to ensure the accuracy of the model, the super parameters in GRU were optimized by arithmetic optimization algorithm (AOA), and the residual lifetime prediction models of GRU were established according to different fault types. The XJTU-SY standard data set was used to validate the residual lifetime prediction, compared with the traditional method of extracting the degradation characteristics and establishing the prediction model without combining the fault types, the experimental results show that the root mean square error and mean absolute error of VMD-GRU model are reduced by 21.26% and 40.15%, respectively.
王通,郗涛,王莉静,等. 基于变分模态分解和门控循环神经网络的滚动轴承剩余寿命预测[J]. 科学技术与工程, , ():复制