Call for papers

Special Sessions

Keynote Speakers







Organized by
Zhejiang University Institute of Computing Innovation, China

Co-Sponsored by
IEEE SMC Society
CSCWD International Working Group
Zhejiang Sci
-Tech University, China

General Conference Chair
Gang Chen

General Conference Co-Chairs
Xiaoping Liu
Jano de Souza
Amy Trappey
Marco Borges
Yong Tang

Program Committee Co-Chairs
Weiming Shen
Jean-Paul Barthès
Junzhou Luo

Publication Chair
Jinghui Zhang

Special Session Chair
Haibin Zhu

Finance Chair / Treasurer
Kunkun Peng

Local Arrangement Chairs
Sai Wu
Jinfeng Gao

International Steering Committee

Jean-Paul Barthès
Junzhou Luo
Weiming Shen

Jinghui Zhang

Pedro Antunes
Marcos Borges
Kuo-Ming Chao
Jano de Souza
Susan Finger
Giancarlo Fortino
Liang Gao
Ning Gu
Anne James
Peter Kropf
Weidong Li
Xiaoping P. Liu
Xiaozhen Mi
Hugo Paredes
José A. Pino
Yanjun Shi
Amy Trappey
Chunsheng Yang
Yun Yang
Jianming Yong
Qinghua Zheng



    Speaker: Ruqiang Yan, Professor of the School of Mechanical Engineering, Xi’an Jiaotong University, China, Fellow of IEEE and ASME

    Title: Physics Model Meets Data Science: A Collaborative Perspective for Intelligent Operation and Maintenance of High-end Equipment



    The Prognostics and Health Management (PHM) system provides the full life-span management of complex high-end equipment, realizing its intelligent operation and maintenance in the era of Industry 4.0. Fault diagnosis & prognosis, as key elements of the PHM system, are undergoing tremendous change. Nowadays, data-intensive science, led by deep learning, has broken through the limitations of physics models on big data and become an important paradigm for diagnosis & prognosis. However, due to the lack of an intuitive understanding of physics models, data science faces challenges in terms of interpretability and reliability. As two ways of observing the laws of the physical world, data science and physics models are not opposite, but two sides of one coin. This presentation will focus on a collaborative perspective of data science with physics models for fault diagnosis & prognosis through collaborative deep learning structures: physics-constraint network and dynamic governing network. This collaborative perspective has merits in aspects of interpretability, controllability, and knowledge discovery, which can further capture the evolution trend of physical systems in the big data era.

    Biographical Sketch:

    Ruqiang Yan is a Full Professor of the School of Mechanical Engineering, Xi’an Jiaotong University, China. His research interests include data analytics, AI, and energy-efficient sensing and sensor networks for the condition monitoring, fault diagnosis and prognosis of large-scale, complex, dynamical systems.

    Dr. Yan is a Fellow of IEEE (2022) and ASME (2019). His honors and awards include the IEEE Instrumentation and Measurement Society Technical Award in 2019, the New Century Excellent Talents in University Award from the Ministry of Education in China in 2009, and multiple best paper awards. Dr. Yan is the Editor-in-Chief of the IEEE Transactions on Instrumentation and Measurement, an Associate Editor of the IEEE Sensors Journal, and Editorial Board Member of Chinese Journal of Mechanical Engineering.