Home A sequence-to-sequence remaining useful life prediction method combining unsupervised LSTM encoding-decoding and temporal convolutional network 博达教师个人主页系统

李嘉琳

讲师 Supervisor of Master's Candidates

Gender : Male

Alma Mater : 东北大学

Education Level : 博士研究生毕业

Degree : 博士学位

Discipline : 机械电子工程

Business Address : 机电实验楼206室

Contact Information : 13840336921

Email :

Research Projects

A sequence-to-sequence remaining useful life prediction method combining unsupervised LSTM encoding-decoding and temporal convolutional network

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First Author : Jialin Li

Date of Publication : 2022-11-01

Indexed by : 应用研究

Translation or Not : no

Correspondence Author : Jialin Li, Renxiang Chen and Xianzhen Huang

Author : Jialin Li

Pre One : Development of Deep Residual Neural Networks for Gear Pitting Fault Diagnosis Using Bayesian Optimization

Next One : A Bayesian Optimization AdaBN-DCNN Method With Self-Optimized Structure and Hyperparameters for Domain Adaptation Remaining Useful Life Prediction

Personal Profile

详细信息见课题组网站：https://www.labxing.com/-10300

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