Navy Artificial Intelligence Maintenance System (AIMS)
Award last edited on: 1/14/2022

Sponsored Program
Awarding Agency
DOD : Navy
Total Award Amount
Award Phase
Solicitation Topic Code
Principal Investigator
Christopher Bowman

Company Information

Data Fusion & Neural Networks LLC (AKA: Data Fusion & Neural Networks~DF&NN))

17150 West 95th Place
Arvada, CO 80007
   (720) 872-2145
Location: Single
Congr. District: 07
County: Adams

Phase I

Contract Number: N68335-20-F-0083
Start Date: 11/21/2019    Completed: 4/20/2020
Phase I year
Phase I Amount
The DF&NN team proposes to demonstrate the feasibility of artificial intelligence (AI) deep neural net machine learning to predict required maintenance activities for Naval aircraft. We estimate the approach will automatically detect and predict anticipated maintenance activities and discover previously unknown required maintenance for aircraft. The system will continue to improve over time as new data are used to re-train and update prediction models. Operational deployment of this capability will be well-suited for at-sea limited connectivity to shore with onboard prediction and fleet-wide updating when in port. The team will deliver a prototype based on an in-place prototype that has been tested on five years on USAF C-130 aircraft engine, pilot debrief, and maintenance/repair data. DF&NN will apply their operationally-proven neural network development platform which has been successfully applied in numerous machine learning environments. The capability includes normal engine behavior learning, historical signature clustering with automated cluster labeling and abnormality class-categorization NN training. These NNs support on-line unknown abnormality detection and known abnormality categorization to enable discovery of repair correlations for predictive maintenance. The team provides an affordably extendable and automatically retrainable C-130 Goal-Driven Condition-Based Predictive Maintenance (GCPM) capability and decades of AI and Navy experience to reduce risk.

Phase II

Contract Number: N68335-20-F-0590
Start Date: 4/29/2020    Completed: 11/5/2021
Phase II year
Phase II Amount
The DF&NN team proposes to further develop the AIMS prototype developed and tested under Phase I to perform predictive maintenance on Naval aircraft. Technical efforts will include improved machine learning performance, all-data source input from Navy sources, customized Navy maintenance personnel user interface and additional trust scoring of predictions. We plan to apply the AIMS Deep Multi-Start Residual Training (D-MSRT) NNs, Smoking Gun, and maintenance condition categorization D-MSRT NNs capabilities for as many aviation systems as available. We will train D-MSRT abnormality detection NNs to learn the labeled repair conditions that were used for each categorization NN to provide a categorization NN result trust score to the user. We will incorporate into AIMS our existing goal-driven turnkey NN capabilities that determine when to retrain, what data to retrain on, what data to test on, how to evaluate, and when to promote to on-line operations. This allow AIMS to automatically evolve and improve its performance based on progressing user goals. We will adapt the AIMS graphical user interfaces (GUI) for user-tier roles with a standardized software deployment approach designed for ease of deployment and upgrade (i.e., Docker REpresentational State Transfer (REST) API services) which support sharing of NNs and results across distributed operations. We will use these to validate AIMS performance and increase user trust in AIMS results. We will work closely with the sponsor to identify operational transition opportunities. AIMS will not be a black box solution. An objective of AIMS is to provide a system that develops trust with operators and provides CBM capabilities. Our approach will be consistent with the strategy: “The purpose of the CBM strategy is to perform maintenance only when there is an objective evidence of need, while ensuring safety, equipment reliability, equipment availability, and reduction of total ownership cost. The fundamental goal of CBM is to optimize readiness while reducing maintenance and manning requirements.” Deployment of AIMS capability will allow the Naval Aviation Enterprise (NAE) to implement CBM within the Naval Aviation Maintenance Program (NAMP) in a deliberate and phased manner. Initially running in parallel with time and operating hour-based inspections, AIMS will provide early detection and characterization of system anomalies and component failures. As the NAE gains confidence in AIMS performance, aircraft systems not critical to safety of flight could be transitioned from schedule-based maintenance to CBM. Once proven, AIMS would facilitate transition of all appropriately instrumented aircraft systems to CBM.