TECHNOLOGY AREA(S): Air Platform

ACQUISITION PROGRAM: PMA231 E-2/C-2 Airborne Tactical Data System

The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with section 3.5 of the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws.

OBJECTIVE: Develop a toolset that would leverage machine learning and analytics to analyze the system design with Automated Logistics Environment (ALE) data collected across the fleet to design and develop improved maintenance procedures that will improve readiness.

DESCRIPTION: The E-2D Advanced Hawkeye has an onboard data retrieval system, the ALE, that monitors and records all bus communications, systems sensors, and built-in test capability on every flight the aircraft makes, from the time the power is turned on until engines are shut down in highly variable operating environments. After each flight, the maintenance personnel review the data using the E-2D ALE viewing tool to assist in identifying maintenance required to be performed. Each flight file is stored for historical and analytical purposes. The ALE environment, although more of a receive and display system, has the potential to provide a deeper analytical capability. The longer the systems are in the fleet, product teams are discovering the need to have ALE provide side-by-side and overlay comparisons of performance metrics. ALE can currently do this, albeit only through a manual process using the ALE viewing tool to see faults or trends on that aircraft.� Built-in test (BIT) trending needs to be more comprehensive in the sense that one BIT failure has value but seen side by side with associated systems that trend within the same flight parameters provides greater system health and diagnostics. For instance, if the historical data could be queried to display the last 20 flights of a particular aircraft and accumulate a percentage of confidence above average BIT failures, associated trends could help determine more accurately the location of the issue. Another example is if at 10K feet, humidity increases in the dome, and temperatures increase in the amplifiers, this could indicate the presence of accumulating water. ALE has limited capability to view data from multiple flights or aircrafts. Each product team has a system(s) with critical components that need up front metrics for that flight, and the ability to see that system�s cumulative flight BIT trending. From here the vision is endless if ALE were able to reach back into supply and work order data. By analyzing factors such as heat/vibration and operational usage Navy personnel could better understand "bad actors", which are the problematic aircraft with chronic low reliability and potentially the greatest single driver of readiness. Data mining can assist maintenance troubleshooting by analyzing bit-code data generated during flight; then maintainers can be provided alternate paths in hard-to-troubleshoot cases. By analyzing the data, the product team can understand the normal operating parameters and then could potentially provide warnings for catastrophic incidents by detecting/predicting these incidents before they occur. Through predictive models using ALE data, there could be some potential to consolidate scheduled maintenance actions. Sample data will be provided during Phase II.

Work produced in Phase II may become classified. Note: The prospective contractor(s) must be U.S. owned and operated with no foreign influence as defined by DoD 5220.22-M, National Industrial Security Program Operating Manual, unless acceptable mitigating procedures can and have been implemented and approved by the Defense Security Service (DSS). The selected contractor and/or subcontractor must be able to acquire and maintain a secret level facility and Personnel Security Clearances, in order to perform on advanced phases of this project as set forth by DSS and NAVAIR in order to gain access to classified information pertaining to the national defense of the United States and its allies; this will be an inherent requirement. The selected company will be required to safeguard classified material IAW DoD 5220.22-M during the advanced phases of this contract.

PHASE I: Utilize mock up, wireframes, and conceptual design to identify areas within Automated Logistics Environment (ALE) that support trend analysis capability as it relates to supportability, reliability and ultimately maintainability of the E-2D platform. Design and demonstrate the feasibility of a toolset to analyze ALE data. Determine key performance metrics of the platform that are of value to the maintainer, IPT, and the enterprise and not necessarily a single solution. The Phase I will include prototype plans to be developed under Phase II.

PHASE II: Develop a prototype software toolset capable of machine learning, data mining, and identifying trends to improve maintenance procedures and readiness.� Identify whether this is a web-based solution or a closed loop effort as IT framework, methodologies, and technologies determine the sustainment barriers once fielded. Adhere to agnostic, non-proprietary, interoperable and best industry development processes/ technologies as this will ensure seamless integration of the toolset. Demonstrate the prototype toolset.

Work in Phase II may become classified. Please see note in Description for details.

PHASE III DUAL USE APPLICATIONS: Finalize development and perform testing. Transition the technology and integrate the final developed toolset into the E-2D ALE. The prototype tool set could be used for commercial aircraft to improve maintenance procedures and readiness. The automotive industry, construction, or any industry utilizing vehicles would benefit from this technology development.

REFERENCES:

1. Hess, A., Calvello, G., and Dabney, T.� �PHM A Key Enabler for the JSF Autonomic Logistics Support Concept.� IEEE Aerospace Conference 2004 (IEEE Cat. No.04TH8720): Big Sky. https://ieeexplore.ieee.org/abstract/document/1368171/

2. Lee, J., Bagheri, B., and Kao, H. �Recent Advances and Trends of Cyber-Physical Systems and Big Data Analytics in Industrial Informatics.� International Conference on Industrial Informatics (INDIN), Cincinnati, OH, 2014. https://pdfs.semanticscholar.org/d217/d5cfe218845da76852ce21fb46499e5c972b.pdf

3. Reis, G., and Saha, A. �Watson Content Analytics: How Cognitive Computing is Transforming Aircraft Maintenance.� MRO Americas, April 2017. http://mromarketing.aviationweek.com/downloads/mro2017/presentations/IBM-HowCognitiveComputingisTransformingCommercialAircraftMaintenance.pdf

KEYWORDS: Aircraft; Maintenance; Logistics; Machine Learning; Readiness; ALE