Connect signals
Vibration, temperature, runtime, current draw, alarms and operator events are evaluated together.
Industrial Edge AI · Predictive Maintenance
Maintenance becomes plannable before the plant stops.
Edge-based predictive maintenance connects sensor data, machine states, load profiles and operating events. AI detects patterns locally and prioritizes maintenance hints where downtime is expensive.
Normal Drift Pattern Risk Maintenance window recommended
Prioritize risks
AI separates harmless deviation from patterns that historically led to downtime.
Act locally
Warnings, limits and local rules keep working even when the central connection is unstable.
Sensor dataMachine stateHistorical patternProbabilityRecommended action
Implementation
From first data stream to maintenance model.
The first step is not a huge AI program. It is a clean data path: one critical asset, relevant signals, normalized timestamps and a local scoring logic.
- Select assets and failure modes
- Check signal quality, sampling and data gaps
- Move models, rules and thresholds into operations