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Service & Maintenance · Service
Data & Classical Analytics / Predictive Maintenance
Failure prediction in service and asset support
Live Core high 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
How Predictive Maintenance Is Improving Manufacturing
Description: General Motors uses IoT sensors and AI to monitor assembly line robots for early wear signs at Saginaw Metal Casting Operations, cutting unplanned downtime by 15% and saving $20 million yearly in maintenance.



Predictive Maintenance Case Studies: How Companies Are Saving Millions with AI-Powered Solutions
Description: Frito-Lay deploys predictive systems using vibration, ultrasound, and oil analysis to detect issues like blower motor faults and oil degradation, reducing planned downtime to 0.75% and unplanned to 2.88% at their Fayetteville plant.



How Predictive Maintenance Is Improving Manufacturing
Description: Mondi applies PdM to plastic extruder at Munich plant via condition monitoring, preventing shutdowns that cost up to €50,000 each, saving €50,000-€80,000 through lower costs and less waste.
https://em360tech.com/tech-articles/how-predictive-maintenance-improving-manufacturinghttps://www.provalet.io/guides-posts/predictive-maintenance-case-studies
Sources
GE Vernova APM — ; SKF Enlight — ; ISO 13374-1 —
https://www.gevernova.com/software/products/apmhttps://www.skf.com/group/products/condition-monitoring-systemshttps://www.iso.org/standard/21832.html