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Tech Talk: Predictive Maintenance Digital Twins in Industrial Systems
Tonight’s Talk
Machine Learning and Artificial Intelligence (ML/AI) have shown great success in consumer applications and have been the main drivers for growth and innovation in the past decade. Industrial applications are fast catching up resolving their own unique set of technical, regulatory, and scalability challenges that have limited direct transferability of ML/AI as is. Significant advancements have been made in inspection, virtual sensing, dynamic process optimization, remote monitoring and predictive maintenance through digital twins of these assets. Digital Twins utilize asset and process data, physics models, domain knowledge, analytics and artificial intelligence technologies to enable continuous learning software representations to provide deep insights to optimize business outcomes. However, full end-to-end deployment with system-wide coverage and autonomy still remains an elusive goal in industrial settings. Specifically, capabilities to safe-guard against unknown-unknowns, lack of explainability and trust tend to be the key bottlenecks. This session will illustrate various industrial application examples and how these challenges are being progressively addressed.
About the Speaker
Abhinav Saxena, PhD
Abhinav Saxena is a Principal Scientist in AI & Learning Systems at GE Research. Abhinav has been developing ML/AI-based PHM solutions for various industrial systems (aviation, nuclear, power, and healthcare) at GE and has been driving integration of AI-based PHM analytics in GE’s industrial systems. Abhinav is also an adjunct professor in the Division of Operation and Maintenance Engineering at Luleå University of Technology, Sweden. Prior to GE, Abhinav was a Research Scientist with SGT Inc. at NASA Ames Research Center for over seven years. Abhinav’s interests lie in developing PHM methods and algorithms with special emphasis on deep learning and data-driven methods in general for practical prognostics. Abhinav has published over 100 peer reviewed technical papers and has co-authored a seminal book on prognostics. He actively participates in several SAE standards committees, IEEE prognostics standards committee, and various PHM Society educational activities, and is a Fellow of the PHM Society. He also served as chief editor of International Journal of Prognostics and Health Management between 2011-2020. Abhinav actively participates in organization of PHM Society conferences and various AI workshops on topics of Digital Twins and AI in Industrial applications.