SBIR Phase I: A Fully Autonomous Prognostic Digital Twin for Smart Manufacturing

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project seeks to assist industries reduce their downtime for scheduled, preventative maintenance.Industries with high-value assets like manufacturing facilities, engines, satellites, reactors, etc., often incur significant expense due to a lack of usable insights into productivity optimization. The forecasting technology and the developments stemming from this project will have general applicability and enable the use of prescriptive prognostics (when and what to repair) in diverse markets. Additionally, the methods developed in the project for training deep learning systems on limited data would have broad application within the machine learning (ML) community. Frequently, projects are limited by access to and availability of data. The methods developed in this project could be applied to small sets of medical data or financial data, as they are entirely defined on time series variables and dynamics. _x000D_ _x000D_ This SBIR Phase I project has two main goals. First, to develop a technology that will enable full autonomy in the extraction of meaningful feature sets from raw sensor data. An autonomous feature selection procedure developed in this project will exploit the combination of powerful control-theoretic results with modern ML tools to discover non-obvious linear and nonlinear features. This solution will provide a physics-informed architecture, allowing users to incorporate available physics knowledge with that emerging from the data, configuring a robust, flexible, and autonomous feature extraction mechanism. Second, the team will construct a robust, multi-modal, sensor emulator to address data insufficiency in order to train the ML components. This opportunity is in response to the limited availability of data in manufacturing sector, especially time-series sensor data in operational systems. The sensor emulator will be formed via combinations of modern ML-based generative tools in a manner that exploits their proven effectiveness while being able to work with high-dimensional signals and small training datasets._x000D_ _x000D_ This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.