I/UCRC FRP: Collaborative Research on Event-based Analytics for Enhanced Prognostics Design in a Big Data Environment

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The proposed work seeks to investigate event-based modeling to deal with high-dimensional and heterogeneous data environments in order to enhance prognostics design with adaptive control of data collection and rapid maintenance decision-making to apply data analytics to the software development process. The event-based approach is explored given its potential to reduce temporal redundancy while preserving the machine dynamics. Event-based approaches have not been fully explored for prognostics applications with continuous signal inputs, such as sensor measurements. The proposed approach represents a paradigm shift in data modeling prognostic system design and holds the potential to help address the fundamental issues of big data in the areas of volume, velocity and variety. Results will be validated using various data collected from a fleet of electric vehicles. The outcomes of the proposed work have the potential for significant impact in the manufacturing sector in the area of prognostics and health monitoring. The resulting approach has the potential to create more efficient systems that can more rapidly adapt and respond to critical issues. The work is supported by the Industry Advisory Board as well as individual industry members of the center and has the potential to extend the centers portfolio through expansion into the area of event-driven modeling, big data reduction and mining for improved industrial efficiency. The center will involve graduate students and undergraduates in the work.