摘要
针对基于浅层学习的轴承寿命预测模型非线性学习能力差、预测精度低的问题,提出一种基于堆叠门控循环神经网络(SGRU)的伺服电机滚动轴承剩余寿命预测方法。首先对轴承振动信号进行时域和时频域特征提取,将常用的时域特征参数和经过集合经验模态分解得到的时频域特征参数作为原始特征集,然后采用相似度度量方法选取最能反映轴承退化性能的特征。之后通过堆叠两层GRU隐层来构建一种深层的寿命预测网络,并以训练集的退化特征参数为输入对网络进行训练,不断优化网络参数。最后在FEMTO数据集上与单层长短期记忆网络(LSTM)方法进行对比。结果表明,该方法相比于单层LSTM方法具有更高的预测精度。
Aiming at the problems of poor nonlinear learning ability and low prediction accuracy of shallow prediction models for the remaining useful life of rolling bearings,a method for predicting remaining useful life of servo motor rolling bearings based on stacked gated recurrent unit(SGRU)was proposed.Time-domain and time-frequency domain features were extracted from bearing vibration signals.The common time-domain feature parameters and time-frequency domain feature parameters obtained through ensemble empirical mode decomposition were taken as the original feature set.After that,the similarity measurement method was used to filter the original feature set to get the best feature reflecting the bearing degradation performance.Then,the deep bearing life prediction model was built by stacking two layers of GRU hidden layers,and the network was trained with degradation characteristic parameters as input and the network parameters were optimized continuously.Finally,the proposed method was compared with the LSTM method on the FEMTO dataset.The experimental results show that the proposed method has higher prediction accuracy than the LSTM method.
作者
尹柏鑫
袁小芳
杨育辉
谢黎
YIN Baixin;YUAN Xiaofang;YANG Yuhui;XIE Li(College of Electrical and Information Engineering,Hunan University,Changsha Hunan 410082,China;National Engineering Laboratory of Robot Vision Perception and Control Technology,Changsha Hunan 410082,China)
出处
《机床与液压》
北大核心
2022年第12期153-158,共6页
Machine Tool & Hydraulics
基金
国家重点研发计划资助项目(2017YFB1300900)。
关键词
集合经验模态分解
门控循环神经网络
剩余寿命预测
滚动轴承
Ensemble empirical mode decomposition
Gated recurrent unit
Remaining useful life prediction
Rolling bearing
作者简介
尹柏鑫(1996-),男,硕士研究生,研究方向为机械故障诊断与寿命预测。E-mail:670763891@qq.com。
