A Generic Framework for Data Model Fusion

Abstract

This report was prepared for the Office of Naval Research in fulfillment of Task 1 of contract N00014-20-C-1099, titled “Data-Model Fusion for Naval Platforms and Systems.” The objective of Task 1 is to develop a rigorous, generic framework for naval applications of Data-Model Fusion. Data-Model Fusion (DMF) is a concept developed at Martin Defense Group (legacy Navatek), in conjunction with the University of Michigan, to describe: 1. Data: information from sensors, expert knowledge, reports, inspections, surveys, or other sources regarding physical components, systems, platforms or fleets within available operating conditions 2. Model: digital representations (e.g., empirical equations, physics-based models, networks, ontological characterizations, etc) of those components, systems, platforms or fleets within simulated operating conditions. 3. Fusion: the integration of said data and models to bring both into agreement. Our datamodel fusion approach uses data science techniques and machine learning methods to improve state estimates, update model parameters, identify operational areas or anomalies, and inform decision-making. Our integration approach utilizes and expands upon the state-of-the-art in data science and Artificial Intelligence (AI)-based decision support to provide real-time actionable diagnostic and/or prognostic information on the state of real-world physical platforms. When a digital model is operationally coupled via sensors to a specific real-world component, system, platform, or fleet, we refer it as a digital twin. Use cases for twins involve managing degrading systems, improving performance, updating design approaches, and optimal planning for a fleet of similar platforms. Digital twins are not a necessary component in data-model fusion, but they are frequently used as a basis for analysis and decision-making in our real-world system applications. This report is a compilation of six separate reports, with an overall focus on defining a fundamental framework for data-model fusion in the naval domain. We start in Chapter 1 with a literature survey of approaches, gaps, and opportunities in data-model fusion. Next, we define in Chapter 2 a unified theory of digital twins, followed in Chapter 3 by a delineation of digital twin types in the naval domain. In Chapter 4 we discuss techniques for data persistence that enable storage of the geometry models, measurements, and environmental data needed by twins. In Chapter 5 we shift our focus back to data-model fusion, providing a survey of tools and techniques that can be used to inform naval systems design and operation. We develop methods for understanding and managing the implications, risks, and opportunities of digital naval engineering with respect to the design and operation of autonomous naval platforms and systems. Chapter 6 was written to serve as a primer on AI-based decision support methodologies, tools, and techniques for practicing naval research engineers and scientists. Finally, we conclude this report with a discussion on technology transfer, capability gaps, and opportunities for further research.