Predictive Maintenance

Unplanned equipment failure is the single largest source of operational loss in manufacturing and facility management. Traditional maintenance schedules are either too frequent (wasting resources) or too infrequent (risking breakdowns).

How Twinise Solves This

Twinise sensors continuously monitor vibration signatures, operating temperatures, and power draw across your critical assets. The platform's analytics layer identifies deviations from baseline patterns — flagging early-stage bearing wear, motor imbalance, or thermal anomalies before they escalate to failure.

What You Get

• Vibration trend monitoring — Vibration nodes report RMS level, peak acceleration, and dominant frequency, giving you a per-asset trend line to watch for change.
• Thermal trending — Track temperature drift across motors, transformers, and mechanical systems. Receive alerts when components leave configured threshold ranges.
• Power consumption correlation — Cross-reference energy draw with operational state. Increased consumption under normal load often signals mechanical degradation.
• 3D twin overlay — Visualise asset health status directly on your Unreal Engine digital twin. Colour-coded indicators show real-time condition across your entire facility.

Recommended Hardware

The Asset Health Use-Case Pack includes vibration sensors, temperature nodes, and power meters pre-configured for predictive maintenance workflows. Pairs with the Starter Kit (10 devices) or Site Kit (50 devices) depending on fleet size.

Expected Outcomes

Organisations deploying Twinise for predictive maintenance typically see a reduction in unplanned downtime, extended equipment lifespan through condition-based intervention, and lower maintenance labour costs by eliminating unnecessary scheduled inspections.