Denver, Colorado

Photo Courtesy of Visit Denver

Sponsored by the IEEE Reliability Society

Program

Welcome to 2025 IEEE Conference on Prognostics and Health Management

Denver, CO
June 9-11, 2025
ICPHM 2025 will be a hybrid event and remote presentation will be an option

Keynotes

"Recent Advances of Industrial AI Augmented Predictive Metrology and Large Knowledge Model for Resilient Industrial Systems"

Keynote speaker: Dr. Jay Lee
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Bio: " Dr. Jay Lee is Clark Distinguished Professor and Founding Director of Industrial AI Center in the Mechanical Engineering of the Univ. of Maryland College Park. His current research is focused on developing non-traditional machine learning technologies including transfer learning, domain adaptation, similarity-based machine learning, stream-of-x machine learning, as well as industrial large knowledge model (ILKM), etc. In addition, he is leading AI Foundry and Data Foundry which consist of over 30 different machine learning analytic tools and 100 diversified industrial datasets including semiconductor manufacturing, jet engines, wind turbine, EVs, high speed train, machine tools, robots, medical TBI, etc. for rapid development and deployment of AI. Previously, he was the founding director of National Science Foundation (NSF) Industry/University Cooperative Research Center (I/UCRC) on Intelligent Maintenance Systems (www.imscenter.net) in partnership with over 100 global company members and the Center was selected as the most economically impactful I/UCRC in the NSF Economic Impact Study Report in 2012. He mentored his students and developed a number of start-up companies including Predictronics through NSF iCorps in 2013. He has developed Dominant Innovation® methodology for product and service innovation design. He is a member of Global Future Council on Advanced Manufacturing and Production of the World Economics Council (WEF), a member of Board of Governors of the Manufacturing Executive Leadership Council of National Association of Manufacturers (NAM), Board of Trustees of MTConnect, as well as a senior advisor to McKinsey. He served as Vice Chairman and Board Member for Foxconn Technology Group (during 2019-2021 and had advised Foxconn business units to successfully receive six WEF Lighthouse Factory Awards. He also served as Director for Product Development and Manufacturing at United Technologies Research Center (now Raytheon Technologies Research Center) as well as Program Director for a number of programs at NSF. He was selected as 30 Visionaries in Smart Manufacturing in by SME in Jan. 2016 and 20 most influential professors in Smart Manufacturing in June 2020, and received SME Eli Whitney Productivity Award and SME/NAMRC S.M. Wu Research Implementation Award in 2022. His new book on Industrial AI was published by Springer in 2020. He is also a working group member for the recent Report on AI Engineering by NSF Engineering Research Visionary Alliance (ERVA) in 2024. He also serves as Editorin-Chef for IOP Science Journal Machine Learning: Engineering. "
Abstract: " This presentation will introduce the trends and recent advances of Industrial AI for improved resilience of complex and highly connected industrial systems. First, trends of data-centric industrial systems and unmet needs of productivity are introduced. Next, some recent advances of industrial AI and non-traditional machine learning including topological data analytics, stream-of-quality (SoQ) based data analytics, similarity-based machine learning, domain adaptation and transfer learning, etc. for highly connected and complex industrial systems will be introduced with some examples including electronics manufacturing, semiconductor manufacturing, EVs, etc. Furthermore, the development of Industrial Large Knowledge Model for enhanced data-centric engineering education will be discussed. Finally, we will address the training industrial AI skills through data foundry for future workforce and talents. "

"Signal Processing Informed Neural Network for Intelligent Fault Diagnosis"

Keynote speaker: Dr. Ruqiang Yan
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Bio: " 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 First Prize for Technological Invention in Shaanxi Province in 2020, the First Prize for Natural Science from the Ministry of Education in 2020, the 2019 IEEE Instrumentation and Measurement Society Technical Award, and the 2022 IEEE Instrumentation and Measurement Society Distinguished Service 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. "
Abstract: " The conventional process of fault diagnosis involves two main steps: feature extraction and decision-making. However, with the emergence of deep neural networks, a more efficient data- driven approach for feature extraction has become available. Despite their universal approximation capabilities, neural networks present challenges in terms of interpretability and achieving optimal solutions due to their extensive parameter space. To tackle these issues, this talk presents a novel type of neural network called Signal Processing Information Neural Networks (SPINN). By incorporating prior knowledge from signal processing, SPINN represents a promising approach to fault diagnosis by effectively merging the power of deep neural networks with the insights from signal processing, ultimately leading to improved performance and better interpretability. "