The Naval Air Technical Data and Engineering Service Center (NATEC) maintains extensive, separate data sets for logistics and engineering in multiple formats which would be more valuable if the data could be integrated and shared to a greater degree. AVNIK Defense Solutions, Inc. proposes, with our subcontract team members Engenix, EPS, and Instrumental Sciences, Inc. (ISI), a Concurrent Engineering Logistics Layered Structure called CELLS concept. CELLS is based on multiple artificial intelligence (AI) technologies of data mining, qualitative reasoning, and machine learning, to support the Navys goal of digital transformation. CELLS will employ a resilient network of autonomous cooperative intelligent agents, to rapidly extract engineering and field data from disparate sources, including JEDMICS, IETMS, and TMAPS, and provide the data as refined information to multiple users, with enhanced eXtended Reality 3D views based on user roles. We successfully integrate our SMEs with analytics and model development to demonstrate the feasibility and potential for a resilient intelligent network to rapidly extract, compile, analyze, and present 3D information derived from disparate Navy databases relevant to users across the Naval Aviation Enterprise. Primary objectives of the Phase II research are to 1) Develop a limited web-enabled CELLS prototype toolset to identify and retrieve technical data from Interactive Electronic Technical Manual (IETMs), technical drawings (JEDMICS), and operating instruction (NATOPS), using an AI-based network architecture, interfaces with NAVAIR Standard IETM Viewer (NSIV), and simulated interfaces with Navy sources (e.g., DECKPLATE, VECTOR); 2) Develop the means to display and retrieve the information above based on role and location, using 3D digital models of selected systems and eXtended Reality (XR) to create a maintenance work package and facilitate use of technical data for both operational maintenance and maintenance training in a variety of settings, using standard Navy IT system components such as desktops, laptops and approved XR devices; and 3) Test and demonstrate the CELLS prototype toolset in an AVNIK systems integration laboratory (SIL). The tests will demonstrate scalability to a full system and verify CELLS ability to: create an interactive work package; capture maintenance and part history; use disparate data sources for predictive maintenance; and provide part and maintenance task traceability for later predictive analysis.
Benefit: Phase II of this SBIR project will result in concept design, feasibility evaluation, and demonstration of a web enabled toolset, called CELLS, for enterprise-wide V-22 aircraft support. Our approach creates a collaborative environment for predictive maintenance analysis, part history traceability, and improved technical data accuracy. CELLS connects engineers, supply chain management, manufacturing, maintainers, operators, and technical data managers with the best available information for aircraft sustainment. This structure improves awareness and decisions across the enterprise, decreasing error and delay and ultimately improving readiness of the V-22 fleet. CELLS enables valuable collaboration by engineering and maintenance IPTs for system design optimization, revisions to maintenance procedures, training, and continuous improvement.
Keywords: Extended Reality, Virtual Technical Data, Concurrent Engineering, Predictive maintenance, Artificial Intelligence, Cooperative Intelligent Agents, Predictive Analytics, Machine Learning
