{"id":3124,"date":"2023-02-16T00:26:17","date_gmt":"2023-02-16T00:26:17","guid":{"rendered":"https:\/\/ieeecscai.wpengine.com\/2023\/?page_id=3124"},"modified":"2023-05-18T06:02:05","modified_gmt":"2023-05-18T06:02:05","slug":"abhinav-saxena","status":"publish","type":"page","link":"https:\/\/cai.ieee.org\/2023\/abhinav-saxena\/","title":{"rendered":"Abhinav Saxena"},"content":{"rendered":"\t\t
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Abhinav Saxena
\nPrincipal Scientist, Machine Learning, GE Research

\nAI in Energy<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\"\"<\/p>

June 6, 10:00am
Location: Santa Clara II<\/h5>

Role of AI in Enabling Carbon Free Energy Transition through Predictive Maintenance<\/span><\/h4>

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. 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 AI\/ML application examples and how these challenges are being progressively addressed as applied to energy industry. Our discussion will be in the context of reducing O&M costs in nuclear power plants where run-time robustness of ML models is key to remote monitoring and risk-informed predictive maintenance due to heavily regulated environment.<\/p>

This work was funded in part by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy, under GEMINA program Award Number DE-AR0001290<\/i><\/p>

\"\"<\/a>Dr. Abhinav Saxena<\/strong> is a Principal Scientist in the Machine Learning Group at GE Research. Abhinav has been developing ML\/AI-based Digital Twins for various industrial systems (aviation, nuclear, power, and renewables) at GE. Digital twins to monitor performance and optimize operations and maintenance over systems\u2019 lifecycles enable improved efficiency and sustainability of critical infrastructure. Abhinav is also an adjunct professor in the Division of Operation and Maintenance Engineering at Lule\u00e5 University of Technology, Sweden. Prior to GE, Abhinav was a Research Scientist at NASA Ames Research Center for over seven years. 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.<\/span><\/p>

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Abhinav Saxena Principal Scientist, Machine Learning, GE Research AI in Energy June 6, 10:00amLocation: Santa Clara II Role of AI in Enabling Carbon Free Energy Transition through Predictive Maintenance 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 […]<\/p>\n","protected":false},"author":5,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":"","_members_access_role":[],"_members_access_error":""},"class_list":["post-3124","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/pages\/3124","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/comments?post=3124"}],"version-history":[{"count":0,"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/pages\/3124\/revisions"}],"wp:attachment":[{"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/media?parent=3124"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}