EAGER/Cybermanufacturing Systems: Fleet-Sourced Cyber Manufacturing Applications for Improved Transparency and Resilience of Manufacturing Assets and Systems

University Of Cincinnati Main Campus, Cincinnati OH

Investigators

Abstract

Internet-enabled services (such as cloud-based and mobile applications) have been influential through almost all economic sectors, such as retail, music, transportation, and healthcare, which have proven the benefit of performing analytics on historical data from a networked system. However, compared to existing Internet-enabled industries, manufacturing assets are less connected and less accessible in real-time. As a result, current manufacturing enterprises make decisions following a top-down approach: from overall equipment effectiveness to assignment of production requirements, without considering the condition of machines. This EArly-concept Grant for Exploratory Research (EAGER) award supports fundamental research to develop the concepts and theory for next-generation advanced cybermanufacturing systems that are networked and interoperable through analytics on the fleet-sourced data. Cybermanufacturing systems will enable a bottom-up real-time decision support manufacturing strategy by taking into account asset health conditions predicted based on historical asset data. Eventually, mobile applications will be developed for portable access of the actionable information. Since such analytics can be performed on data collected from existing asset condition monitoring systems with moderate levels of add-on sensor installment, manufacturing industries in almost every sector will benefit from the results of this research. Consequently, this research will inject speed into the development of U.S. economy and benefit the society by increasing the efficiency and productivity of manufacturing enterprises. This research requires knowledge and expertise from a variety of disciplines including manufacturing, mechanical engineering, computer science, and control theory. The interdisciplinary methodology will facilitate the creativity and healthy growth of the involved areas and draw interest from younger generation to impact science, technology, engineering and mathematics education. The new cybermanufacturing methodology will deepen the research on fleet-sourced prognostics, which overcomes several drawbacks of conventional prognostics and health management approaches, including lack of generality and reconfigurability, lack of robustness against changing regimes, and sometimes insufficient accuracy. A fleet is referred to as a group of assets similar in working conditions (make and model, ambient conditions, and health status). Research gaps in conducting fleet-sourced prognostics exist in the quantification of asset similarity, clustering fleets, validation of such fleets, and dynamically changing the clustering scheme when regimes change. The research team will leverage existing fleet-level peer-to-peer prognostics approaches to develop a reconfigurable platform with capabilities to reduce the dimensionality from fleet-sourced data, devise a risk assessment methodology to provide real-time predictive actionable information, and eventually incorporate such functions into the developed mobile applications.