Session 2: From Models to Missions: Making PHM Work at Fleet Scale by Dr. Abhinav Saxena скачать в хорошем качестве

Session 2: From Models to Missions: Making PHM Work at Fleet Scale by Dr. Abhinav Saxena

Abstract: Prognostics and Health Management (PHM) has reached an inflection point. Over the last two decades, the community has delivered powerful foundations for understanding degradation, building diagnostic and prognostic models, estimating remaining useful life, and evaluating PHM performance. Yet in many real deployments, the barriers to impact are no longer primarily algorithmic. They are operational: inconsistent and incomplete data, shifting operating regimes across geographies and operators, edge-versus-cloud constraints, cybersecurity and governance requirements, regulatory differences, and the practical reality of maintaining models over years without an unsustainable version-control burden. Drawing on 15–20 years of experience spanning foundational PHM research at NASA and industrial-scale deployment challenges at GE Research, this webinar argues that the next leap in PHM value will come from treating deployment as a first-class technical problem. We will explore why “model once, deploy everywhere” fails for globally operated assets such as jet engines, power generation systems, transportation systems, or medical imaging equipment —and what it will take to build PHM systems that generalize, remain trusted, and continuously prove value in real operations. The talk will close with a forward-looking agenda that bridges near-term deployment priorities with areas where new foundational research is still urgently needed. Bio: With over two decades of PHM experience, Abhinav Saxena is a Principal Scientist in AI at GE Aerospace Research. He is passionate about transitioning AI-driven PHM from promising models into deployable, fleet-scale solutions across aviation, power (including nuclear), and healthcare. He has led the integration of PHM analytics into industrial systems operating in diverse environments and under real-world constraints, including data variability, infrastructure limitations, governance requirements, and lifecycle sustainment. He served as PI for the ARPA-E GEMINA program on AI-enabled predictive maintenance digital twins for advanced nuclear reactors and has led multi-organization, government-funded PHM programs across agencies. Previously, Abhinav spent more than seven years at NASA Ames Research Center (SGT Inc.), focusing on foundational research in degradation, diagnostics, and prognostics—work that shaped his perspective on moving PHM from lab results to operational impact. He has authored 120+ peer-reviewed publications and co-authored a seminal book on prognostics. A founding member and Fellow of the PHM Society, he has contributed extensively through standards work (SAE, IEEE), education, and leadership, including serving as Editor-in-Chief of the International Journal of Prognostics and Health Management (2011–2020). Note: The information in this video is provided for educational and informational purposes only. The PHM Society and the speaker are not responsible for any errors, omissions, or results obtained from the use of this information. Any action you take based on this content is strictly at your own risk. Always use proper safety precautions, follow relevant guidelines or regulations, and consult a qualified professional if you are unsure. By watching and applying the information in this video, you agree that PHM Society and speaker shall not be liable for any damages, injuries, or losses arising from its use.