Predictive Maintenance
Predict machine failures before they get expensive.
Predictive maintenance continuously assesses equipment wear from sensor and machine data. Instead of fixed maintenance intervals, interventions happen exactly when needed, cutting both unplanned downtime and maintenance spend.
- Fewer costly emergency repairs
- Higher equipment availability
- Better planning for maintenance crews
unplanned downtime
maintenance costs
payback period
early-warning lead time
Calculated from historical downtime costs per asset and the expected reduction in unplanned failures.
- 01
Asset assessment
Identify critical machines and existing sensors.
- 02
Data connection
Feed PLC and historian data into a central time-series store.
- 03
Model development
Train failure patterns per component using historical fault data.
- 04
Pilot operation
Validate the model in shadow mode on selected machines.
- 05
Rollout & integration
Integrate alerts into the CMMS and adapt maintenance workflows.
Steps
Data sources
Stakeholders
From first data access to production – every step delivers a tangible interim result.
PLC signals
Operating parameters straight from the controller.
Historian data
Long-term time series of temperature, pressure, vibration.
IoT gateways
Retrofitted sensors on legacy equipment.
CMMS history
Past maintenance and fault records as training data.
Spare-parts consumption
ERP consumption data correlated with wear.
Environmental data
Temperature and humidity as influencing factors.
Maintenance manager
Plans interventions instead of reacting to failures.
Plant management
Gets full transparency on availability and cost.
IT/OT leads
Gains a clean, reusable data foundation.
Controlling
Can plan maintenance budgets more reliably.
Fewer costly emergency repairs
Higher equipment availability
Better planning for maintenance crews
Longer asset lifetime
Lower spare-parts inventory through targeted ordering
With a solid data foundation this use case gets faster, cheaper and far more stable.
Central time-series database for all machine signals
Standardized onboarding of new machines without custom work
Automated data-quality checks right at the source
Reusable feature pipelines for further models
Predictive quality
The same sensor data also flags quality deviations.
Inventory optimization
Predictions directly inform spare-parts stock levels.
Digital twin
Wear models feed directly into simulation twins.