Data & Classical Analytics / Predictive Maintenance
Failure prediction in service and asset support
LiveCorehigh effect
Core capability
The system predicts likely failures in advance, so maintenance can be planned instead of triggered by breakdowns, reducing unplanned stoppages and improving asset availability.
How it works
The system learns what early equipment degradation looks like from past failures and service history, then looks for the same warning patterns in live data so maintenance can be planned before breakdowns occur.
Application here
Models analyze asset data and predict failures before they happen, supporting planned maintenance across the installed fleet.
Business impact
This can reduce unplanned downtime, improve spare-parts planning, and support more efficient service operations.
Limitations
It depends on consistent field data and enough historical failures. It cannot reliably predict misuse, external events, or truly novel degradation mechanisms.
In production
This is already used where breakdowns are expensive: the system helps teams service equipment based on predicted condition instead of waiting for failure.
Research
The frontier is moving toward models that can learn from many sites and many machines at once, stay physically realistic, and improve predictions without forcing every plant to share raw data.
Examples
GE Vernova APM predicts gas turbine failures 10–15 days ahead, serving 74 000+ units (GE Vernova product brief). SKF Enlight AI predicts bearing wear in industrial fans and pumps, enabling the shift from scheduled to condition-based maintenance — .