基于CNN与注意力BiLSTM的轴承剩余使用寿命预测方法

Prediction method of bearing remaining useful life based on CNN and attention BiLSTM

滚动轴承的剩余使用寿命(Remaining Useful Life,RUL)预测对于旋转机械的运行和维护具有重要意义.以卷积神经网络(Convolutional Neural Network,CNN)为代表的深度学习方法虽然可以从轴承振动信号中自动提取特征,却不能对特征进行自适应的选择以提高模型对重要特征的关注程度.针对上述问题,提出一种基于CNN和注意力双向长短期记忆网络(Bidirectional Long Short-Term Memory,BiLSTM)的轴承RUL预测方法.首先通过CNN对振动信号进行空间特征提取;然后利用BiLSTM提取时序特征;接着利用注意力机制增强模型对重要特征的关注程度,并以全连接层作为解码器预测健康指标(Health Indicator,HI);最后利用加权平均法对HI预测值进行修正,并利用多项式拟合曲线进行RUL预测.结果表明,所提方法的绝对百分比误差比卷积长短期记忆网络(CNN-LSTM)低14.36%,比传统的自组织映射网络(SOM)低21.28%,可用于多故障模式下的RUL预测.

The remaining useful life(RUL)prediction of rolling bearings is of great significance for the operation and maintenance of rotating machinery.Although the deep learning method represented by convolutional neural network(CNN)can automatically extract features from bearing vibration signals,it can not adaptively select features to improve the attention of the model to important features.In response to the above problems,a bearing RUL prediction method based on CNN and attention bidirectional long short-term memory(BiLSTM)was proposed.Firstly,the spatial features of vibration signals were extracted by CNN,the temporal features were extracted by BiLSTM.Then,the attention mechanism was used to enhance the attention of the model to important features,and the full connection layer was used as the decoder to predict the health indicator(HI).Finally,the weighted average method was used to modify the HI prediction value,and the polynomial fitting curve was used for RUL prediction.The results show that the absolute percentage error of the proposed method is 14.36%lower than that of convolutional long short-term memory(CNN-LSTM)and 21.28%lower than that of traditional self-organizing map network(SOM),which can be effectively used for RUL prediction in multiple fault modes.