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Charles River Analytics, a provider of autonomy technologies, is developing advanced predictive maintenance and logistics technologies for the United States Navy’s ship systems.
These technologies are designed to improve reliability across the full lifecycle of complex naval assets, including ships, fleets, and supporting equipment. A multidisciplinary team is applying system modeling, hybrid AI reasoning, and cognitive systems engineering to create software services that can predict system performance and maintenance needs before failures occur.
This work is being funded by the Naval Sea Systems Command (NAVSEA) and supported through a series of contracts totaling $6.6 million over 8.5 years.
The software will deliver on-platform, real-time prognostics and diagnostics, along with actionable insights for operators and maintainers. The solution includes a back-end analytics engine that applies probabilistic programming to forecast failures and assess operational risk.
It combines domain expertise with sensor and log data through a hybrid AI approach and translates complex technical information into clear maintenance recommendations via a user-focused decision support interface.
These predictive analytics technologies help junior technicians quickly identify potential problem areas and prioritize maintenance tasks. This capability allows the Navy to strategically deploy technical specialists when their advanced expertise is truly needed.
The team is addressing a fundamental challenge with predictive systems and AI, helping users understand how the system arrives at its conclusions.
Mandy Warren, UX Senior Scientist at Charles River Analytics, commented, “We’re not framing the information from a system engineering perspective, but from a perspective where maintenance staff can interpret the maintenance picture. Our end users greatly appreciate that they don’t need the same understanding as the engineer who architected the system; they only need to know what’s relevant and what they need to do in that moment.”
Enabling Predictive Maintenance for Naval Ship Systems
Traditional maintenance approaches rely on fixed schedules to replace degraded parts or respond after failures occur. This reactive model can result in wasted resources, untimely maintenance, and operational delays.
Effective logistics and timely parts availability are critical for long-duration or hard-to-reach assets, such as ships at sea, where system failures can have severe consequences and maintenance windows are limited.
Kenny Lu, Machine Learning Scientist at Charles River Analytics, commented, “By predicting when failures occur, you can optimize resource and labor allocation by prioritizing the failures or degradations that are most pressing or most impactful for the mission.”
The Navy is shifting toward a more proactive maintenance approach that uses data to anticipate needs before catastrophic failure. The software is designed to support this change.
After more than eight years of development and testing, the predictive maintenance system is now moving from research to operational use, with a prototype soon to be deployed on a Naval ship. By reducing unnecessary maintenance, the technology frees up the Navy’s resources for mission-critical needs and enhances operational readiness through early failure prediction and prevention.
Charles River Analytics is also exploring opportunities to apply its predictive maintenance and logistics technologies beyond the Navy, including other military and commercial domains such as ground and air autonomy, oil and gas, power grids, and industrial maintenance. The focus remains on making complex analytics accessible to non-engineers through improved system trust and user-friendly interfaces.
