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AI for the Depot: Using ETAR for Digital Health Records

Description:

TECH FOCUS AREAS: General Warfighting Requirements (GWR)

 

TECHNOLOGY AREAS: Materials; Air Platform

 

OBJECTIVE: This effort will deliver a prototype Digital Health Record application to provide all Engineering Technical Assistance Request (or ETAR) information, non-destructive inspection (NDI) results, Airworthiness information and 3D images to determine the health of each aircraft. The Digital health record application will provide an organized and indexed data for Artificial Intelligence & Machine Learning for disposition decisions/actions and contribute to predictive Maintenance.

 

DESCRIPTION:  This project will include: 1. Designing and prototyping a Digital Health Record application to provide all ETAR information, NDI results, Airworthiness information and 3D images to determine the health of KC-135 2. Identify all relevant data sources and connect disparate data to create relationships to expand and operationalize AI/ML and 3. Use machine learning, historical performance data and contextual data to predict maintenance and alert for proactive identification of problem parts.  While the data is currently being tracked, it is not analyzed to help make informed planning decisions- and this in this case KC-135 does not use the data to decide when to retire a plane.  Engineering dispositions are burdened with repetitive assistance requests and responses, incorrect entry, lack of standards and quality. With increased data standards and quality, trending on historical mx actions it can result in faster/more accurate disposition. With the addition of technology and build out of the aircraft technical baseline (as sustained), the data can begin to be aggregated and analyzed to show predictive results.

 

PHASE I: This topic is intended for technology proven ready to move directly into a Phase II. Therefore, a Phase I award is not required. The offeror is required to provide detail and documentation in the Direct to Phase II proposal which demonstrates accomplishment of a “Phase I-like” effort, including a feasibility study. This includes determining, insofar as possible, the scientific and technical merit and feasibility of ideas appearing to have commercial potential.

 

PHASE II: Eligibility for D2P2 is predicated on the offeror having performed a “Phase I-like” effort predominantly separate from the SBIR Programs. Under the Phase II effort, the offeror shall sufficiently develop the technical approach, product, or process in order to conduct a small number of advanced manufacturing and/or sustainment relevant demonstrations. Identification of manufacturing/production issues and or business model modifications required to further improve product or process relevance to improved sustainment costs, availability, or safety, should be documented. Air Force sustainment stakeholder engagement is paramount to successful validation of the technical approach. These Phase II awards are intended to provide a path to commercialization, not the final step for the proposed solution.

 

PHASE III DUAL USE APPLICATIONS: The contractor will pursue commercialization of the various technologies developed in Phase II for transitioning expanded mission capability to a broad range of potential government and civilian users and alternate mission applications. Direct access with end users and government customers will be provided with opportunities to receive Phase III awards for providing the government additional research & development, or direct procurement of products and services developed in coordination with the program.

 

REFERENCES:

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  7. Kim, Y., Lee, J., Lee, E.-B., Lee, J.-H. Application of Natural Language Processing (NLP) and Text-Mining of Big-Data to Engineering-Procurement- Construction (EPC) Bid and Contract Documents (2020) Proceedings - 2020 6th Conference on Data Science and Machine Learning Applications, CDMA 2020, art. no. 9044209, pp. 123-128. DOI: 10.1109/CDMA47397.2020.00027

 

KEYWORDS: Artificial Intelligence (AI); Machine Learning (ML)

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