N221-029 TITLE: DIGITAL ENGINEERING - Artificial Intelligence /Machine Learning Applications to STANDARD Missile Maintenance Data

OUSD (R&E) MODERNIZATION PRIORITY: Artificial Intelligence (AI)/Machine Learning (ML)

TECHNOLOGY AREA(S): Weapons

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 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: Apply Artificial Intelligence (AI)/Machine Learning (ML) techniques to develop a decision aide that automates and modernizes STANDARD Missile (SM) maintenance processes and procedures with the goal of reducing life cycle costs and manpower while maintaining readiness.

DESCRIPTION: AI/ML has seen steady growth in the commercial market. Consumers see daily benefit in many areas; greater computing power as Intel, Apple, and other manufactures incorporate AI into computer processors; predictive analytics used for targeted marketing on Google and other platforms. A survey conducted by McKinsey Analytics in 2020 indicates, since 2018 greater adoption of AI principles in the manufacturing (e.g., yield, optimization, predictive maintenance) and supply chain management industries while simultaneously demonstrating 10-20% cost savings. PEO IWS and Naval Supply System Command (NAVSUP) are specifically interested in AI/ML applications in these areas, applied to the SM family.

The SM family are solid propellant, tail-controlled surface to air missiles. Variants of SM have been in production for over 20 years. For maintenance and recertification, missiles cycle through an Intermediate Level Maintenance Facility (ILMF) at NMC Seal Beach and a Depot Level Maintenance Facility (DLMF) at the Missile manufacturer, Raytheon Missiles and Defense. NSWC Corona collects missile and section level data through all maintenance and recertification periods on a Surface Missile Systems Maintenance Data System (SMSMDS) database. The SMSMDS collects, stores, and distributes missile life cycle data, including (for current and previous variants) All Up Round manufacturing and production baseline performance data, test data, re-certification reports, Trouble Reports, and Failure Reporting and Corrective Action System (FRACAS). The technology sought will enable the SM program office to optimize maintenance concepts and strategies. This in turn will allow for increased capability to the warfighter and has the potential to reduce life cycle costs by prioritizing maintenance activities at the ILMF and DLMF.

Due to the amount of SM maintenance data available, the amount of missile maintenance work required, and current fiscal constraints, the SM program office has a strong desire to use AI/ML to modernize and automate maintenance planning as well as procedures while reducing extensive man hours required to analyze SM readiness and prioritize sustainment activities. The output will serve as a decision aide for the SM program office and will assist in understanding any section level or piece part�s failure�s influence on the overall mission effectiveness of the system.

Currently efficiencies are dependent on personnel experience and reporting. This point of view of efficiency is very narrow and does not factor in other pieces of the entire process. Overarching aggregated views of the entire process is at its infancy. The tool needs to be compatible with SQL server to analyze the current logistical state and an optimized state.

The Navy needs a tool to optimize SM maintenance strategies. The preferred solution will be a tool that uses AI/ML concepts such as linear regression, Decision Tree, best suited to existing SM data, which allows the user to make quicker decisions, predict reliability related failures, and identify future maintenance issues. It should also provide recommended repair processes and procedures. The tool will serve to reduce planning requirements and actions and improve procurement of spares and depot level preparation required to maintain the Fleet required load out requirements and inventory posture. Operation of the tool must be extensible to UNCLASSIFIED U.S. Navy network infrastructure. System required to comply with NIST SP 800-37 standards to include ACAS vulnerability scans and system hardening utilizing relevant DISA STIG�s (i.e., Application & Security STIG and applicable OS STIG). Awardees will be required to coordinate with Government representative for specific cyber requirements.

PHASE I: Develop a concept for a SM maintenance decision aide that meets the parameters of the Description. Demonstrate the concept feasibility through analysis, modelling, and simulation. The Phase I Option, if exercised, will include the initial design specifications and a capabilities description to build a prototype solution in Phase II.

PHASE II: Develop and deliver a prototype SM maintenance decision aide based on the results of Phase I. Demonstrate the prototype meets the required range of desired performance attributes given in the Description. System performance will be demonstrated through installation and prototype testing on a testbed with the lead system integrator. The system will be checked for data accuracy of recorded values versus stored/calculated values. The algorithm results of the training data set will be evaluated against new data. The system optimizing capabilities metrics will be used in practice to check for concurrence.

PHASE III DUAL USE APPLICATIONS: Support the Navy in transitioning the AI/ML tools to Navy use in the SM maintenance program to improve repair processes, procurement of spares, and depot level preparation required to maintain the Fleet required load out requirements and inventory posture. Support employing the technology developed under this SBIR topic to the Navy SM field activities. Assist in the transition of the data analytics into actionable maintenance plans and strategies for the SM program. Explore the potential to transfer the optimizing algorithm to other military and commercial systems such as automotive, aerospace, shipping, and manufacturing where logistical planning is needed.

REFERENCES:

1. Matthew Montoya. "Standard Missile: A Cornerstone of Navy Theater Air Missile Defense"; Johns Hopkins APL Technical Digest, Vol 22 Number 3. 2001 P. 234-247. https://www.jhuapl.edu/Content/techdigest/pdf/V22-N03/22-03-Montoya.pdf.
2. Diogo Cardoso, Luís Ferreira. "Application of Predictive Maintenance Concepts using Artificial Intelligence Tools"; Applied Sciences, 2021-01-01, Vol.11 (1), p.18, https://www.researchgate.net/publication/347857580_Application_of_Predictive_Maintenance_Concepts_Using_Artificial_Intelligence_Tools.
3. Zeki Murat Cinar, Abubakar Abdussalam Nuhu, Qasim Zeeshan, Orhan Korhan, Mohammed Asmael, Babak Safaei. "Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0"; Sustainability (Basel, Switzerland), 2020-10-01, Vol.12 (19), p.8211, https://www.mdpi.com/2071-1050/12/19/8211.
4. Michael Chui, Tara Balakrishnan, Bryce Hall, Nicolaus Henke, (n.d.). "The State of AI in 2020" McKinsey & Company. https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/global-survey-the-state-of-ai-in-2020.

KEYWORDS: Intermediate Level Maintenance Facility; Artificial Intelligence; Machine learning; STANDARD Missile; STANDARD Missile Maintenance; Decision Aide