学术动态
当前位置: 学院首页 > 学术动态 > 正文

Hybrid operational digital twins for complex systems: Fusing physics-based and deep learning algorithms for fault diagnostics and prognostics

国际机械中心“智能制造、检测与诊断”系列学术讲座通知(四)

发布时间:2022-10-17 点击数:

讲座人:

Olga Fink has been assistant professor of intelligent maintenance and operations systems at EPFL since March 2022. Before joining EPFL faculty, Olga was assistant professor of intelligent maintenance systems at ETH Zurich from 2018 to 2022, being awarded the prestigious professorship grant of the Swiss National Science Foundation (SNSF). Between 2014 and 2018 she was heading the research group “Smart Maintenance” at the Zurich University of Applied Sciences (ZHAW) where she was senior lecturer. Olga received her Ph.D. from ETH Zurich on the topic of “Failure and Degradation Prediction by Artificial Neural Networks: Applications to Railway Systems” and a diploma in industrial engineering from the Hamburg University of Technology. She has gained valuable industry experience as a reliability engineer for railway rolling stock and as a reliability and maintenance expert for railway systems. Olga’s research focuses on DataDriven ConditionBased and Predictive Maintenance, Physics-Informed Machine Learning for Operational Digital Twins, Deep Learning and Decision Support Algorithms for Fault Detection, Diagnostics and Prognostics of Complex Industrial Assets.


讲座简介:

Deep learning algorithms need large amounts of representative data to learn relevant patterns. Although increasing amounts of condition monitoring data have been recently collected for complex systems, these data lack labels (in form of faults) and often also representativeness due to the high variability in operating conditions.  Integrating physics and structural inductive bias helps to overcome some of the limitations of deep learning algorithms. It reduces the amount of required training data, adds interpretability in the algorithms and makes some of the problems solvable that were not solvable before. Furthermore, it helps to build trust in the algorithms by making the outputs interpretable.

The talk will give some insights into hybrid operational digital twins developed by fusing physics-based and deep learning algorithms for fault diagnostics and prognostics.

Zoom 会议 : 

https://zoom.us/j/83774179016?pwd=T3krenNJT1RSS0FMWHJGU05uTWN0QT09

讲座时间:2022年10月20日 04:00 下午 北京   

会议号:837 7417 9016


  • 附件【Fink's talk.pdf】已下载

地址:陕西省西安市咸宁西路28号 邮编:710049
           版权所有:EMC易倍体育官方 - emc易倍体育网页版     站点维护: 网络信息中心 陕ICP备06008037号