山东煤炭科技2024，Vol.42Issue(2)：95-98,108,5.DOI:10.3969/j.issn.1005-2801.2024.02.021
基于长短期记忆神经网络的采煤机摇臂轴承剩余寿命预测
Prediction of Residual Life of Coal Mining Machine Rocker Arm Bearings Based on Long Short-Term Memory Neural Network
王振环1
作者信息
- 1. 山西潞安化工集团司马煤业有限公司,山西 长治 047100
- 折叠
摘要
Abstract
To solve the failure problem of key components of coal mining machine rocker arms,an innovative method based on Long Short-Term Memory(LSTM)neural network is proposed to predict the residual life of the coal mining machine rocker arm bearing.Based on the theory of Long Short-Term Memory neural network,by establishing a degradation index for bearing life,the residual life of the bearing is conducted to predict.Isomorphism uses stratified sampling method to partition data sets;By introducing particle swarm algorithm to optimize LSTM,the problem of selecting the optimal hyperparameter in LSTM algorithm is solved,and the accuracy of predicting the residual life of the bearing is improved.The research results indicate that the residual life predicted results of the bearing based on LSTM is basically consistent with the actual changes situations in bearing life,and the predicted results are all within the confidence interval,which can provide reference for bearing maintenance and upkeep work.关键词
长短期记忆神经网络/采煤机摇臂轴承/剩余寿命Key words
Long Short-Term Memory neural network/coal mining machine rocker arm bearing/residual life分类
矿业与冶金引用本文复制引用
王振环..基于长短期记忆神经网络的采煤机摇臂轴承剩余寿命预测[J].山东煤炭科技,2024,42(2):95-98,108,5.
