噪声与振动控制2024，Vol.44Issue(3)：95-100,145,7.DOI:10.3969/j.issn.1006-1355.2024.03.015
基于MRSDAE-KPCA结合Bi-LST的滚动轴承剩余使用寿命预测
Residual Useful Life Prediction of Rolling Bearings Based on MRSDAE-KPCA Combined with Bi-LSTM
摘要
Abstract
Aiming at the problem that the existing rolling bearing residual useful life prediction methods do not fully consider the internal distribution of the data when extracting the data features,and the artificial extraction of expert experience is also required when constructing the health factors,a rolling bearing residual useful life prediction method based on manifold regularization stack denoising autoencoder(MRSDA),kernel principal component analysis(KPCA)and combined with bi-directional long short-term memory(Bi-LSTM)network is proposed.Firstly,the unsupervised stack denoising autoencoder network is used to extract the deep features of the original vibration data,and the KPCA is used to further reduce the dimension to improve the index stability of the health factors.Then,manifold regularization is added to the stack denoising autoencoder to maximize the data distribution structure within the coder's hidden layer and improve the effectiveness of the model in extracting data features.Finally,the Bi-LSTM network is used to predict the residual useful life of the bearing,and the AdaMax optimization algorithm is used to adaptively optimize the super parameters of the network model.The analysis results show that the proposed method for predicting the residual useful life of rolling bearings has higher accuracy.关键词
故障诊断/滚动轴承/剩余使用寿命预测/健康因子/流形正则化堆栈去噪自编码器/双向长短时记忆网络Key words
fault diagnosis/rolling bearing/residual useful life prediction/health factors/manifold regularization stack denoising autoencoder/bi-directional long short-term memory network分类
机械制造引用本文复制引用
古莹奎,陈家芳,石昌武..基于MRSDAE-KPCA结合Bi-LST的滚动轴承剩余使用寿命预测[J].噪声与振动控制,2024,44(3):95-100,145,7.基金项目
国家自然科学基金资助项目(61963018) （61963018）
江西省自然科学基金重点资助项目(20212ACB202004) （20212ACB202004）
