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

The full program will soon become available

Keynotes

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

Keynote speaker: Dr. Jay Lee

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

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. "

Tutorials

"Introduction to PHM Theory and Practice"

Tutorial speaker: Dr. Stephen Johnson

Bio: " Dr. Stephen Johnson is the President of Dependable System Technologies, LLC, and the general editor for System Health Management: with Aerospace Applications (2011). His PHM experience includes being the control system fault protection engineer on the Magellan deep space probe to Venus in the 1980s, the head of Martin Marietta Astronautics Vehicle Health Management research in the early 1990s, a co-founder and head of engineering for a small PHM business in the 1990s, a faculty member of the Space Studies Department at the University of North Dakota from 1997 to 2005, the Analysis Lead for Mission and Fault Management on NASA Marshall Space Flight Center’s Space Launch System program and its precursors from 2005 to 2023, and numerous small PHM and systems engineering R&D contracts with government and industry from 2005 to the present. He has authored and co-authored numerous articles and books on system health management and fault management theory and practice, and other topics including systems engineering, space history and economics, and the philosophy of technology. "

Abstract: " This one-day short course introduces the core concepts and practices of Prognostics and Health Management / System Health Management. The class provides an introductory overview of the following: history of and motivation for PHM; core concepts and terminology; fault management functions; goals and requirements; architecture and design issues and strategies; technical performance metrics; and analysis methods. This course can be taken stand-alone or as part of the PHM Standards conference track. "

"SPC methodologies for monitoring and optimizing HFC networks"

Tutorial speakers: Dr. Maher Harb and Nader Foroughi

Bio: " Maher Harb is a Distinguished Engineer at Comcast working on problems at the intersection of Data Science, Machine Learning, and Telecommunications Networks. His research interests include applying Reinforcement Learning to optimize network performance, developing graph-based algorithms for network design & management, building deep neural network models for detection of RF impairments, and developing statistical methods for anomaly detection & root case identification. Prior to Comcast, Maher was an Assistant professor of Physics at Drexel University where he led an experimental Condensed Matter Physics laboratory investigating laser-matter interactions. Maher has a PhD in Physics from the University of Toronto and he held a postdoctoral fellowship at the Swedish National Synchrotron (Max-lab)."

Bio: " Nader is a Distinguished Engineer at Comcast, where he is responsible for access network evolution, artificial intelligence and automation. Prior to joining Comcast, Nader was the Chief Technology Officer of Americas at Technetix, responsible for technology strategy and AI. His career also includes significant contributions at Shaw Communications, where he was responsible for access architecture and technology in conjunction with data sciences, making key advancements in proactive network maintenance, profile management application, and DOCSIS 4.0 development. Nader has a background in mathematics, engineering and systems architecture, with numerous white papers published spanning from DOCSIS 4.0 to applications of deep reinforcement learning in telecommunications."

Abstract: " Statistical Process Control (SPC) and control charts, long-established quality control methodologies in manufacturing and process industries, have recently emerged as powerful tools for monitoring and optimizing Hybrid Fiber-Coaxial (HFC) networks, particularly in Full Duplex DOCSIS environments. Building upon decades of successful implementations in sectors ranging from semiconductor fabrication to pharmaceutical production, this paper presents an adaptation of SPC methodologies to cable network monitoring. Traditional network monitoring approaches relied on global thresholds for network parameters, limiting the ability to detect device-specific anomalies and performance degradation. Our implementation leverages individualized control charts for network devices, enabling dynamic threshold computation based on historical device behavior rather than system-wide metrics. By analyzing device-specific patterns and variations, the methodology demonstrates improved anomaly detection precision compared to global threshold approaches. The individualized control limits facilitate more accurate correlation between network events and performance degradation, particularly in identifying upstream channel impairments and RF interference patterns. Furthermore, the refined granularity of device-level control charts provides higher quality training data for backend machine learning algorithms, reducing false positives and enhancing predictive maintenance capabilities. This approach demonstrates significant potential for improving network reliability and maintenance efficiency in next-generation cable networks through data-driven, device-specific monitoring strategies. "