Digital Twins for Critical Assets — Predict, Optimize, and Extend Performance

A digital twin is a virtual representation of a physical asset, system, or process that is continuously updated with real-world data. By combining models with live sensor inputs, digital twins allow organizations to monitor performance, simulate scenarios, and predict future outcomes with greater accuracy.

Critical assets in energy, defense platforms, and infrastructure operate in high-value, high-risk environments where downtime, failure, or inefficiency can be costly and dangerous. Digital twins provide an elevated view of real-time performance, predictive diagnostics, and lifecycle intelligence that help safeguard reliability, extend asset life, and ensure operational readiness in the toughest environments.

In energy and battery systems, digital twins track performance and health across the entire lifecycle — from lab testing to deployment in the field. They enable real-time monitoring, predictive maintenance, degradation forecasting, and compliance reporting. This improves safety, reduces downtime, and extends asset life. Read more on practical applications of digital twins in battery analytics.

For defense and mission-critical environments, digital twins help predict failures, optimize fleet readiness, and extend lifecycle under complex and high-stakes conditions. By simulating stress scenarios and analyzing performance data, they dramatically reduce risk and improve operational availability.

In infrastructure and smart facilities, digital twins visualize energy usage, simulate upgrades, and model facility operations down to the tenant level. They help facility managers improve efficiency, lower operating costs, and plan for sustainability initiatives.

A traditional simulation models how an asset could behave under certain conditions. A digital twin, on the other hand, is a living model that continuously ingests data from the real asset. This makes it capable of reflecting actual operating conditions in real time and predicting future performance.

Digital twins provide a continuous data thread from raw materials to end-of-life, enabling full traceability. This helps manufacturers and operators comply with evolving regulations (such as IRA and OBBBA), while improving predictive diagnostics and planning for recycling or reuse. Read more.

Examples include real-time monitoring of performance, anomaly detection, forecasting degradation and remaining useful life, simulating stress scenarios for safety, optimizing maintenance schedules, and modeling lifecycle costs. Explore use cases.

Peaxy threads data across design, operations, testing, and recycling. Its digital twin solutions integrate live sensor data with advanced analytics models to deliver diagnostics, performance forecasting, compliance readiness, and lifecycle intelligence. Read more about a battery analytics use case.