Ruqiang Yan, PHD, Professor

Xi'an Jiaotong University

TItle: Knowledge Driven Machine Learning Towards Interpretable Intelligent Prognostics and Health Management

Abstract: Despite significant progress in the Prognostics and Health Management (PHM) domain using pattern learning systems from data, machine learning (ML) still faces challenges related to limited generalization and weak interpretability. This talk presents the latest developments in PHM, encapsulated under the concept of Knowledge Driven Machine Learning (KDML). A hierarchical framework to define KDML in PHM is first introduced. Several cases will then be studied to illustrate specific implementations of KDML in the PHM domain, including inductive experience, physical model, and signal processing. Finally, the challenges, potential applications, and usage recommendations of KDML in PHM, with a particular focus on the critical need for interpretability will be discussed to ensure trustworthy deployment of artificial intelligence in PHM.

Biography: Ruqiang Yan is a Full Professor and Director of International Machinery Center at 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 and health diagnosis of large-scale, complex, dynamical systems.. Dr. Yan is a Fellow of IEEE (2022) and ASME (2019). He is the recipient of several prestigious awards including the 2019 IEEE Instrumentation and Measurement Society Technical Award, and the Andrew P. Sage Best Transactions Paper Award. He has led the development of one IEEE standard and published over one hundred papers in IEEE and ASME journals, and other publications. He was the Editor-in-Chief of the IEEE Transactions on Instrumentation and Measurement, currently serves as an IEEE Instrumentation and Measurement Society Distinguished Lecturer and Associate Editor-in-Chief of Chinese Journal of Mechanical Engineering.