Digital Twin Data Acquisition System for Institutional Facility Management

Emerging Technology Ventures Inc. (ETV) and its research and development (RD) partners, Navajo Technical University (NTU) and New Mexico Institute of Mining and Technology (NMT), proposed to develop and demonstrate a Digital Twin (DT) Data Acquisition System for Institutional Facility Management. The innovation addresses Industry 4.0 digital transformation initiatives in Building Information Modelling (BIM) and Facility Management (FM) which have created critical demand for up-to-date digitized building assets for effective implementation in predictive, condition-based maintenance (CBM) strategies in FM. The teams proposed use of autonomous, multi-modal systems and analytics to create DTs representing near real-time status of the built environment for FM offers an opportunity for responsive, labor efficient CBM. This Phase II proposal continues the work completed in Phase I and results in a deployable capability to NASAs Marshall Space Flight Facility for user evaluation and feedback in an iterative Research-Design-Build process. This project continues collaboration between ETV and its RD partners which have ongoing supporting technology initiatives including the National Aeronautics and Space Administration (NASA) Minority University Research and Education Project (MUREP) Innovation Tech Transfer Idea Competition with NTU and the Department of Defense (DoD) Autonomous Inspection, Damage Classification, and Repair Support System for Aircraft Mission Readiness with NMT. This joint proposal represents the next step in the teams vision to build a fundamental and applied research collaborative in autonomous sensing and predictive analytics in complex environments. The innovation aims to meet market opportunities in critical infrastructure inspection (aerospace, facilities, renewable energy), precision agriculture, and public safety. NASA seeks specific innovative, transformational, model-based solutions in “Digital Twin” (DT) Institutional Management of Health/Automated Decision Support of Agency Facilities. The overarching Model-Based (MBx) DT Enterprise model(s) would significantly enhance operational efficiencies regarding information gathering, risk analysis, and the overall velocity and robustness of knowledge transfer and decision-making across the Agency. These solutions aim to improve the information's quality, robustness, and trustworthiness by identifying and analyzing risks earlier. The innovation addresses NASA’s articulated STTR needs in delivering an end-to-end DT system for integrated BIM and FM. The proposed effort will couple the STTR efforts with the core architecture and neural network engine (NNE) from ETV’s internal R&D and Navy Phase I ADAPT SBIR effort to deliver an overall system capability to support the Phase II STTR. These outcomes will support NASA as it implements its Digital Transformation objectives and moves toward a “Smart Centers” environment for its facility constellation. Technical Objective: Utilize IBM Maximo and Watson tools to visualize sensor data to gain deeper insights into the data. Use knowledge gained from data to plan for developing neural networks to perform predictions on data. Deliverable: Report how we plan to develop neural networks to perform predictions across multiple subsystems. Technical Objective : Integrate neural network predictor into IBM Maximo to interface with the digital twin and data streaming from OSI PI. Deliverable: Demonstrate ability to stream live data into the neural networks to perform real time predictions and show the results of those predictions in the DT. Technical Objective: Develop multiple neural networks, utilizing data insights found from previous objectives to build more accurate systems. This will include using data from sensors on different subsystems that are found to have connections to the subsystems. Deliverable: Demonstrate and report on results from running neural networks on previous time series sensor data. Show accuracy of predictions based on data not previously seen by the network to predict failures. Technical Objective: Create 4D model of fully integrated system to show historical data streams as well as previous predictions and results of those predictions. Deliverable: Create and demonstrate the 4D model using previous time series data to preview abilities of production system.
Benefits: The proposed innovation addresses NASA’s articulated Small Business Technology Transfer (STTR) needs in the delivery of an end-to-end DT system for integrated Building Information Management (BIM) and Facility Management (FM). These outcomes will support NASA as it implements its Digital Transformation objectives and moves towards a “Smart Center” environment for its facility constellation. ETV recently submitted a response to a Sources Sought entitled “Optimizing Facilities Leveraging Digital Twin Using Modeling, Simulation, and Analysis Tools” to the National Center for Manufacturing Sciences (NCMS) on behalf of their US Army customer.