文章摘要
尹柏鑫,袁小芳,杨育辉,谢黎.基于堆叠GRU的伺服电机滚动轴承剩余寿命预测[J].机床与液压,2022,50(12):153-158.
YIN Baixin,YUAN Xiaofang,YANG Yuhui,XIE Li.Prediction for Remaining Useful Life of Servo Motor Rolling Bearing Based on Stacked GRU [J].Machine Tool & Hydraulics,2022,50(12):153-158
基于堆叠GRU的伺服电机滚动轴承剩余寿命预测
Prediction for Remaining Useful Life of Servo Motor Rolling Bearing Based on Stacked GRU
  
DOI:10.3969/j.issn.1001-3881.2022.12.029
中文关键词: 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
英文关键词: Ensemble empirical mode decomposition  Gated recurrent unit  Remaining useful life prediction  Rolling bearing
基金项目:国家重点研发计划资助项目(2017YFB1300900)
作者单位E-mail
尹柏鑫 湖南大学电气与信息工程学院机器人视觉感知与控制技术国家工程实验室 670763891@qq.com 
袁小芳 湖南大学电气与信息工程学院机器人视觉感知与控制技术国家工程实验室  
杨育辉 湖南大学电气与信息工程学院机器人视觉感知与控制技术国家工程实验室  
谢黎 湖南大学电气与信息工程学院机器人视觉感知与控制技术国家工程实验室  
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中文摘要:
      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。
英文摘要:
      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.
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