基于ASFF-AAKR和CNN-BILSTM滚动轴承寿命预测  

Life Prediction Based on ASFF-AAKR and CNN-BILSTM Rolling Bearings

作　　者：张永超[1] 刘嵩寿 陈昱锡 杨海昆 陈庆光[1] ZHANG Yong-chao;LIU Song-shou;CHEN Yu-xi;YANG Hai-kun;CHEN Qing-guang(College of Mechanical and Electronic Engineering,Shandong University of Science and Technology,Qingdao 266590,China)

机构地区：[1]山东科技大学机械电子工程学院,青岛266590

出　　处：《科学技术与工程》2025年第2期567-573,共7页Science Technology and Engineering

基　　金：山东省自然科学基金(ZR2021ME242)。

摘　　要：针对滚动轴承寿命预测精度低,构建健康指标困难的问题。提出了一种基于自适应特征融合(adaptively spatial feature fusion,ASFF)和自联想核回归模型(auto associative kernel regression,AAKR)与卷积神经网络(convolutional neural networks,CNN)和双向长短期记忆网络(bi-directional long-short term memory,BILSTM)的轴承剩余寿命预测模型。首先,在时域、频域和时频域提取多维特征,利用单调性和趋势性筛选敏感特征;其次利用ASFF-AAKR对敏感特征进行特征融合构建健康指标;最后,将健康指标输入到CNN和BILSTM中,实现对滚动轴承的寿命预测。结果表明:所构建的寿命预测模型优于其他模型,该方法具有更低的误差、寿命预测精度更高。To address the issues of low accuracy in rolling bearing life prediction and the difficulty of constructing health indicators,a bearing remaining life prediction model based on ASFF(adaptively spatial feature fusion)and AAKR(auto associative kernel regression)combined with CNN(convolutional neural networks)and BILSTM(bi-directional long-short term memory networks)was proposed.Firstly,the multidimensional features were extracted in the time domain,frequency domain,and time-frequency domain,and the sensitive features were screened using monotonicity and trend.Secondly,the sensitive features were feature fused using ASFF-AAKR to construct the health indicators.Finally,the health indicators were inputted into CNN and BILSTM to realize the life prediction of rolling bearings.The results show that the constructed life prediction model is better than other models,and the method has lower error and higher life prediction accuracy.

关 键 词：滚动轴承 自适应特征融合 自联想核回归 卷积神经网络 双向长短期记忆网络 剩余寿命预测 

分 类 号：TH133.33[机械工程—机械制造及自动化] TP183[自动化与计算机技术—控制理论与控制工程]