June 5, 4:45pm
Location: Magnolia

Physics Informed Neural Nets for Systems Health Management

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Development in data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior.The research work presents application of physics-informed neural nets application to a representative electric powertrain for unmanned aerial vehicles. The model is composed of physics-derived and empirical equations, integrated with connected networks that are strategically placed within the model to substitute equations that are subject to large uncertainty. Polynomial fit driven by heuristics or empirical observations can be substituted by more flexible networks that can minimize the error between model predictions and observations without being restricted to a predefined functional form. This modeling strategy allows training of networks deep inside the model and unknown parameters in a single learning stage. 

Chetan S. Kulkarni is a staff researcher at the Prognostics Center of Excellence and the Diagnostics and Prognostics Group in the Intelligent Systems Division at NASA Ames Research Center. His current research interests are in Systems Diagnostics, Prognostics and Health Management. Specifically focused on developing physics-based and hybrid modeling approaches for diagnosis and prognosis of complex systems. He is KBR Technical Fellow, AIAA Associate Fellow, SMIEEE and Associate Editor for IEEE, SAE, IJPHM Journals on topics related to Prognostics and Systems Health Management. Technical Program Committee co-chair at PHME18, PHM20-23. He co-chairs the Professional Development and Education Outreach subcommittee in the AIAA Intelligent Systems Technical Committee.