基于CNN-GRU模型的齿轮寿命预测

Gear Life Prediction Based on CNN-GRU Model

齿轮是机械运动中非常重要的零部件,一旦发生损坏将对机械整体造成不可估量的损失,甚至波及人身安全,因此提前预知齿轮的剩余寿命非常重要。随着深度学习的快速发展,可以采用深度学习对齿轮进行剩余使用寿命的预测。卷积神经网络(CNN)具有权值共享和局部感知的优点,但是在处理时间序列上,CNN还有一定的缺陷;门控循环单元(GRU)可以处理时间序列在长距离上依赖不足的问题且结构简单。为了既可以具有权值共享的特点又可以解决时间序列的问题,提出CNN-GRU模型来对齿轮进行寿命预测。实验结果表明:使用该方法后准确率和训练速度都得到了提升,具有一定的应用价值。

The gear is a very important part in the mechanical movement, once the gear is damaged, it can cause inestimable losses to whole machinery, even affects personal safety.Therefore, it is very important to predict the remaining life of the gear in advance.With the rapidly development of deep learning, it can be used to predict the remaining useful life(RUL) of gears.Convolutional neural networks(CNN) has the characteristics of weight sharing and local perception, however, it still has some defects in processing time series.Gated recurrent unit(GRU) can deal with the problem of insufficient dependence over long distance of time series and has a simple structure.In order to have the feature of weight sharing and also solve the time series problem, the CNN-GRU model was proposed to predict the gear life.The experimental results show that the accuracy and training speed of this method are improved, and it has certain application value.