常州大学学报（自然科学版）2026，Vol.38Issue(1)：66-73,8.DOI:10.3969/j.issn.2095-0411.2026.01.008
基于向量加权平均算法优化的轴承剩余寿命预测
Remaining life prediction of bearings based on INFO optimization
摘要
Abstract
Aiming at the problem of high complexity of bearing vibration signals,a feature extraction method based on the weighted mean of vectors-variational mode decomposition-variational modal de-composition(INFO-VMD)was proposed.In addition,due to the high variability of bearing vibration signal characteristics,the Deep Extreme Learning Machine Prediction Model with Multi-feature Filte-ring(MFDELM)was proposed to improve the accuracy of prediction.Firstly,the INFO-VMD meth-od was used to find the optimal number of layers and the penalty coefficient,and then the time domain and frequency domain features were extracted from the modal components.Finally,the feature set was input into the MFDELM prediction model to calculate the remaining service life of the bearing.The results of the computer simulations show that the prediction model score is 0.47,which is 0.16 better than the based on LSTM model and 0.21 better than the based on GRU model,and the effec-tiveness of the proposed method is verified through full life Experiment of Bearing.关键词
向量加权平均算法/变分模态分解/滚动轴承/深度极限学习机/寿命预测Key words
weighted mean of vectors algorithm/variational mode decomposition/rolling bearings/deep extreme learning machine/life prediction分类
机械制造引用本文复制引用
周靖诺,郇战,陈瑛,朱学勤..基于向量加权平均算法优化的轴承剩余寿命预测[J].常州大学学报（自然科学版）,2026,38(1):66-73,8.基金项目
国家自然科学基金资助项目(62201093). （62201093）
