摘要
针对变工况环境下采集到的滚动轴承寿命状态数据存在特征分布差异,深度神经网络模型泛化能力差的问题,结合时间卷积网络(temporal convolutional neural network,TCN)和残差自注意力机制提出了一种端到端的滚动轴承剩余寿命(remaining useful life,RUL)迁移预测方法。首先,将传感器采集到的一维时域信号利用短时傅里叶变换转换为频域信号;其次,剩余寿命迁移预测网络通用特征提取层采用残差自注意力TCN网络,该网络在较好提取时间序列信息的同时,进一步通过残差自注意力机制捕获轴承局部退化特征,增强模型的迁移特征提取能力;再次,采用提出的联合领域自适应策略匹配变工况下滚动轴承寿命状态数据特征分布差异,实现不同工况下轴承寿命状态知识的迁移预测;最后,在公开的滚动轴承全寿命数据集上进行试验验证,结果表明所提方法能有效实现变工况下的滚动轴承RUL预测,并获得较好的预测性能。
Here,aiming at problems of feature distribution differences existing in collected life state data of rolling bearing and poor generalization ability of deep neural network model under variable operating conditions,an end-to-end transfer prediction method for remaining useful life(RUL)of rolling bearing was proposed by combining temporal convolutional neural network(TCN)and residual self-attention mechanism.Firstly,one-dimensional time-domain signals collected by sensors were converted into frequency-domain signals with short-time Fourier transform.Secondly,general feature extraction layer of RUL transfer prediction network could use a residual self-attention TCN network.This network could not only better extract time series information,but also capture local degradation features of bearing with the residual self-attention mechanism to enhance the model’s transfer feature extraction ability.Once again,the allied field adaptive strategy proposed could be used to match feature distribution differences of rolling bearing life state data under variable operating conditions,and realizethe transfer prediction of bearing life state knowledge under different operating conditions.Finally,the test verification was conducted on publicly available datasets of rolling bearing full life.The results showed that the proposed method can effectively realize RUL transfer prediction of rolling bearing under variable operating conditions and obtain better predictive performance.
作者
潘雪娇
董绍江
朱朋
周存芳
宋锴
PAN Xuejiao;DONG Shaojiang;ZHU Peng;ZHOU Cunfang;SONG Kai(College of Traffic and Transportation,Chongqing Jiaotong University,Chongqing 400074,China;College of Mechatronics and Vehicle Engineering,Chongqing Jiaotong University,Chongqing 400074,China;College of Mechanical and Vehicle Engineering,Chongqing University,Chongqing 400044,China;Chongqing Chang’an Automobile Co.,Ltd.,Chongqing 401120,China)
出处
《振动与冲击》
EI
CSCD
北大核心
2024年第1期145-152,共8页
Journal of Vibration and Shock
基金
国家自然科学基金资助项目(51775072)
重庆市科技创新领军人才支持计划项目(CSTCCCXLJRC201920)
重庆市高校创新研究群体(CXQT20019)
重庆市北碚区科学技术局技术创新与应用示范项目(2020-6)。
关键词
剩余寿命(RUL)
滚动轴承
时间卷积网络(TCN)
残差自注意力
迁移学习
remaining useful life(RUL)
rolling bearings
temporal convolutional neural network(TCN)
residual self-attention
transfer learning
