基于注意力GRU算法的滚动轴承剩余寿命预测

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振动与冲击 ›› 2021, Vol. 40 ›› Issue (17) : 116-123.

论文

姚德臣1,2，李博阳1,2，刘恒畅1,2，姚娟娟3，皮雁南3

作者信息 +

Residual life prediction of rolling bearing based on attention GRU algorithm

YAO Dechen1,2, LI Boyang1,2, LIU Hengchang1,2, YAO Juanjuan3, PI Yannan3

Author information +

文章历史 +

摘要

针对旋转机械装置中滚动轴承剩余寿命随时间变化趋势难以准确预测问题，充分利用循环神经网络(
recurrent neural networks,RNN)对时间序列数据的处理能力，提出一种融合注意力机制的门控循环单元(attention gated recurrent unit，AGRU)算法应用于滚动轴承剩余寿命预测领域之中。该方法首先从原始振动信号中提取多种时域特征构建数据集，并将数据集进行归一化处理，其次，将注意力机制(attention mechanism)引入GRU(gated recurrent unit)模型之中，最后，将特征数据集划分为训练集和测试集，训练集用于训练模型，确定最优模型参数，测试集用于对模型效果进行评估。
试验结果表明，改进后的 GRU模型可有效预测不同类型的滚动轴承剩余寿命随时间变化趋势，为滚动轴承零件剩余使用寿命预测提供了一种新思路。

Abstract

Aiming at the problem of it being difficult to accurately predict the change trend of rolling bearing residual life with time variation in rotating machinery, making full use of the ability of recurrent neural network (RNN) to process time series data, the attention gated recurrent unit (AGRU) algorithm based on attention mechanism was proposed to predict rolling bearing residual life.Firstly, multiple time-domain features were extracted from original vibration signals to construct a data set, and the data set was normalized.Secondly, the attention mechanism was introduced into GRU model.Finally, the feature data set was divided into a training set and a testing set.The training set was used to train the model and determine the optimal model parameters.The testing set was used to evaluate the effect of the model.The experimental results showed that the improved GRU model can effectively predict the change trend of residual life of different types of rolling bearing with time variation; it can provide a new idea for predicting residual life of rolling bearing components.

导出引用

姚德臣，李博阳，刘恒畅，姚娟娟，皮雁南. 基于注意力GRU算法的滚动轴承剩余寿命预测[J]. 振动与冲击, 2021, 40(17): 116-123

YAO Dechen, LI Boyang, LIU Hengchang, YAO Juanjuan, PI Yannan. Residual life prediction of rolling bearing based on attention GRU algorithm[J]. Journal of Vibration and Shock, 2021, 40(17): 116-123

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