Peaxy Lifecycle Intelligence for Defense Readiness optimizes fleet equipment maintenance for dynamic defense readiness conditions. Recommendations for maintenance and inventory management are powered by digital twins and machine learning algorithms to maximize a fleet’s capability across a wide variety of maintenance regimes, so that it can perform robustly in any readiness condition.
Peaxy Lifecycle Intelligence continuously monitors mission-critical onboard functions and systems including batteries, inverters, propulsion, gearboxes, gas turbines, power conversion and air scrubbing. When the system detects anomalies, it recommends proactive and cost-effective maintenance solutions that are compatible with multiple mission readiness scenarios.
Peaxy technologies in support of defense readiness:
Peaxy Lifecycle Intelligence brings world-class capabilities to tasks that require predicted outcomes using very large, unstructured longitudinal data sets. By predicting when a serialized piece of equipment needs attention, PLI can improve uptime and lower costs for fleets of ships, planes, autonomous vehicles... even submarines.
Mission readiness and maintenance go hand in hand. The CBM Recommendation Engine delivers an optimized maintenance regime compatible with multiple near-term readiness scenarios. Complex background processes informed by both digital twins and machine learning feed into intuitive health scores, alert notifications and clear calls to action for fleet operators.
Digital twins are complex simulations of serialized equipment that combine physics-based, empirical, and semi-empirical models to generate performance predictions from real-time operating data. Peaxy has pioneered digital twinning, deploying one of the industrial world’s first commercially viable digital twins for General Electric in 2016. PLI’s deployed digital twins range across gas turbines, steam turbines, power generators and batteries.
Peaxy's Machine Learning Manager trains, tests and runs a wide variety of algorithms on serialized data sets to pinpoint correlations between manufacturing attributes, operating profiles and performance. Anomalies from expected normal operating ranges are flagged and forwarded to an alerting and recommendation system for preventative maintenance.
The system can be deployed directly to the vessel instead of to the cloud — so that mission readiness does not depend on a functioning internet infrastructure.