Special Session VI

Anomaly Detection, Remaining Useful Life Prediction, and Maintenance: Advanced Theories and Industrial Applications
异常检测、剩余寿命预测于维护决策：先进理论和工业应用

In reliability engineering, numerous techniques and theories have been developed in recent years, aiming to encompass more general cases, complex settings, and larger datasets. This advancement provides an opportunity to address both unresolved classical problems and emerging issues. Special attention is given to anomaly detection, remaining useful life (RUL) prediction, and maintenance decision-making. In anomaly detection, areas of interest include online detection, fault isolation, unsupervised methods, and hybrid physical-statistical methods. For RUL prediction, the focus is on developing new degradation models, multivariate degradation models, among other aspects. For maintenance, the focus is on time series-based predictive maintenance, condition based maintenance, among others. We warmly welcome innovative thinking and cutting-edge methods to address industrial problems related to reliability engineering. Review and tutorial papers are also welcomed.

在可靠性研究中，随着先进技术和理论的蓬勃发展，现有方法已经可以涵盖更广义的假设、考虑更复杂的模型和处理更大量的数据。这为我们提供了解决可靠性工程和工业应用领域中尚未解决的经典问题和新出现的问题的机会。本专题关注但不限于异常检测、剩余寿命预测和维护决策三方面。异常检测的关注问题包括但不限于在线检测、故障隔离、无监督方法、物理-统计混合方法等。剩余寿命预测所关注的问题包括但不限于多元退化建模和新的随机过程模型。维修关注的问题包括但不限于基于时间序列的预防性维修和基于状态的预防性维修。我们非常欢迎针对工业工程相关问题的创新思维和前沿方法。欢迎综述和教程类论文投稿。

Topics of interest for submission include, but not limited to:

●Unsupervised anomaly detection 无监督故障检测
●Image data in anomaly detection 基于图形数据的故障检测
●Physical-statistical hybrid method 物理-统计融合方法
●Multivariate degradation models 多元退化建模
●Online RUL prediction 在线剩余寿命预测
●Stochastic process-based RUL prediction 基于随机过程的剩余寿命预测
●Time series-based predictive maintenance 基于时间序列的预防性维修

Chair: Jiaxiang Cai, Agency for Science, Technology and Research (A*STAR), Singapore

Jiaxiang Cai received the B.E. degree in hydraulic engineering in 2012 and the M.S. degree in environmental science and engineering in 2015, both from Tsinghua University. He received the Ph.D. degree in industrial systems engineering and management in 2021 from National University of Singapore. He is currently a senior scientist at Agency for Science, Technology and Research (A*STAR), Singapore. His research interests include reliability engineering, industrial statistics, AI applications in maritime. (E-mail: caijx@ihpc.a-star.edu.sg)

蔡加祥本科与硕士均就读于清华大学，并于2012和2015年分别获得水利工程学士学位和环境科学与工程硕士学位。博士就读于新加坡国立大学，并于2021年获得工业系统工程与管理博士学位。目前在新加坡国科技研究局（A*STAR）担任资深科学家。 研究方向包括可靠性工程、工业统计、人工智能在海事领域的应用。

Chair: National University of Singapore, Singapore

Bingxin Yan received the B.E. degree in safety engineering in 2018 and the Ph.D. degree in systems engineering in 2023, both from the School of Reliability and Systems Engineering at Beihang University. Her research interests include reliability engineering and applied stochastic processes. (E-mail: yanbingxin124@163.com)

闫冰心本科与博士均就读于北京航空航天大学可靠性与系统工程学院，并于2018年和2023年分别获得安全工程学士学位与系统工程博士学位。研究方向包括可靠性工程与应用随机过程。

 Submission Portal

If you are interested in the special session, please submit your paper or abstract here
https://www.zmeeting.org/submission/srse2024 and choose special session 4.

For any questions, please mail the organizers or conference secretary srse@sciei.org.