轻工机械2025，Vol.43Issue(1)：63-71,9.DOI:10.3969/j.issn.1005-2895.2025.01.009
经EMD处理的DACNN-BiGRU-Attention模型滚动轴承剩余寿命预测
Life Prediction of Rolling Bearings Using DACNN-BiGRU-Attention Model Processed by EMD
摘要
Abstract
Aiming at the problem of the single deep learning model for low precision,complex degradation data,low data dimensionality and large computation in predicting the Remaining Useful Life(RUL)of rolling bearings,a rolling bearing RUL prediction method based on DACNN-BiGRU-Attention was proposed by the research group.Firstly,the characteristic components of the bearing vibration signal were extracted by Empirical Mode Decomposition(EMD)and combined into a high-dimensional data as the input of the Convolutional Neural Networks(CNN).Secondly,the dynamic activation function(Dynamic ReLU)was used in the CNN,achieving adaptive activation for different channels,thereby reducing the computation.Finally,the model introduced the Multi-Head Attention(MHA)mechanism,effectively extracting data information and improving prediction accuracy.The verification results of the DACNN-BiGRU-Attention model processed by EMD on the PHM2012 bearing data set show that the prediction accuracy was improved.The comparative analysis results show that the proposed model has better prediction accuracy than the CNN-BiGRU-Attention,CNN-BiGRU and untreated DACNN-BiGRU-Attention models.关键词
轴承/剩余使用寿命预测/经验模态分解/动态激活卷积神经网络/多头注意力Key words
bearings/RUL(Remaining Useful Life)prediction/EMD(Empirical Mode Decomposition)/DACNN(Dynamic Activation Convolution Neural Networks)/MHA(Multi-Head Attention)分类
机械制造引用本文复制引用
宁少慧,戎有志,董振才..经EMD处理的DACNN-BiGRU-Attention模型滚动轴承剩余寿命预测[J].轻工机械,2025,43(1):63-71,9.基金项目
山西省应用基础研究计划资助(20210302123212). （20210302123212）
