关于举行苏黎世联邦理工学院Olga Fink教授学术报告会的通知

发布时间:2022-01-14设置

报告题目:Towards Intelligent Operation and Maintenance of Complex Systems

报 告 人:Dr. Olga Fink - ETH Zürich(苏黎世联邦理工学院智能维护系统SNSF教授)

报告时间:202211720:0020:55

腾讯会议ID718753799

邀 请 人:李巍华教授(Prof. Weihua Li

联 系 人:陈祝云博士(Dr. Zhuyun Chen, mezychen@scut.edu.cn

欢迎广大师生踊跃参加。

 

 

吴贤铭智能工程学院

2022114

 

 

报告人简介:

Olga Fink has been assistant professor of intelligent maintenance systems at ETH Zürich since October 2018, being awarded the prestigious professorship grant of the Swiss National Science Foundation (SNSF). She is also a research affiliate at Massachusetts Institute of Technology and Expert of the Innosuisse in the field of ICT. Her research focuses on Intelligent Maintenance Systems, Data‐Driven Condition‐Based and Predictive Maintenance, Hybrid Approaches Fusing Physical Performance Models and Deep Learning Algorithms, Deep Learning and Decision Support Algorithms for Fault Detection and Diagnostics of Complex Infrastructure and Industrial Assets. Before joining ETH faculty, she was heading the research group “Smart Maintenance” at the Zurich University of Applied Sciences (ZHAW) between 2014 and 2018. Olga received her Ph.D. degree from ETH Zurich with the thesis on “Failure and Degradation Prediction by Artificial Neural Networks: Applications to Railway Systems”, and Diploma degree in industrial engineering from Hamburg University of Technology. She has gained valuable industrial experience as reliability engineer with Stadler Bussnang AG and as reliability and maintenance expert with Pöyry Switzerland Ltd. In 2018, she was selected as one of the “Top 100 Women in Business, Switzerland”, in 2019, she was selected as young scientist of the World Economic Forum and in 2020 and 2021 as young researcher of the World Laureate Forum. 

 

Olga Fink目前是瑞士苏黎世联邦理工学院智能维护系统SNSF教授,麻省理工学院的研究员,以及瑞士科技创新署信息与通信领域专家组成员。她的研究方向包括:智能维护系统、数据驱动的状态监测和预测性维护、融合物理模型和深度学习的混合智能、复杂工业系统深度学习检测诊断和决策支持方法。

Olga Fink在德国汉堡工业大学工业工程专业获得硕士学位,在苏黎世联邦理工学院获得博士学位,其博士论文题目为“基于人工神经网络的铁路系统性能退化预测方法研究”。加入ETH之前,她于2014年至2018年在苏黎世应用科技大学(ZHAW)领导“智能维护”组开展研究工作。作为瑞士施泰德铁路公司以及瑞士Pöyry公司的可靠性和智能维护专家,Olga拥有非常丰富的工程应用经验。

Olga Fink入选2018“瑞士最杰出商业女性100人”,2019“世界经济论坛青年科学家”,2020/2021“世界顶尖科学家论坛青年科学家”。

 

报告摘要:

The amount of measured and collected condition monitoring data for complex infrastructure and industrial assets has been recently increasing significantly due to falling costs, improved technology, and increased reliability of sensors and data transmission. However, faults in safety critical systems are rare. The diversity of the fault types and operating conditions makes it often impossible to extract and learn the fault patterns of all the possible fault types affecting a system. Consequently, faulty conditions cannot be used to learn patterns from. Even collecting a representative dataset with all possible operating conditions can be a challenging task since the systems experience a high variability of operating conditions. Therefore, training samples captured over limited time periods may not be representative for the entire operating profile. The collection of a representative dataset may delay the implementation of data-driven fault detection and isolation systems. Moreover, some of the current limitations include the limited scalability, generalization ability and interpretability of the developed models. The talk will give an overview of the currently ongoing research at the chair of Intelligent Maintenance Systems at ETH Zürich, including 1) research in the field of domain adaptation and unsupervised transfer learning for fault detection and diagnostics at fleet level, 2) research on algorithms combining deep learning and physics-based approaches; 3) research in prescriptive operations and 4) research on multi-agent systems for decision support and maintenance scheduling. 

 

随着硬件成本的下降、技术的改进以及传感器与数据传输可靠性的提高,复杂基础设施获取的状态监测数据量逐渐增加。然而,一方面工业系统无法在故障状态下运行,导致实际故障样本难以获取;另一方面机械系统故障模式复杂、运行工况多变,要采集所有运行工况下的故障模式非常具有挑战性,所获取的特定时段数据难以反应机械系统整体运行状况。若需采集更多故障数据,可能会导致现有数据驱动的故障检测系统执行延迟;此外,所开发的模型还会受模型的复杂程度、泛化能力以及可解释性等的限制。

报告将对当前ETH智能维护中心研究组的最新研究进展进行系统的介绍,主要包括:1)最新的域适配技术、无监督迁移学习技术在机械状态监测和故障诊断领域的应用;2)融合物理模型和深度学习模型的混合方法;3)运筹学方法研究;4)用于智能调度与决策支持的多智能体系统研究。

 

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