Predictive Maintenance & Condition Monitoring
Detect abnormal machine behavior before it becomes downtime.
The industrial problem
Unexpected machine failures stop production, and time-based maintenance either comes too late or wastes resources on healthy machines. The signals of degradation are usually in the data – but nobody is watching thousands of data points around the clock.
How it works
We collect the signals that reflect machine health – joint currents, motor load, temperature, vibration, cycle time, machine states, alarm history, speed, torque, and energy consumption – and analyze them continuously. The system learns normal operating patterns, detects abnormal behavior, identifies gradual degradation, and compares machines and production cycles.
We use realistic industrial language on purpose: this is anomaly detection, condition monitoring, trend analysis, and early warning – practical decision support for maintenance teams, not a promise that every failure can be predicted.
Typical data sources
- Robot joint currents and motor load
- Temperature and vibration
- Cycle time and machine state
- Alarm history
- Speed and torque
- Energy consumption
- Production interruptions
- Quality trends
Business value
- Early warnings before failures escalate
- Reduced unexpected downtime
- Maintenance planned by condition, not by calendar
- Faster root-cause investigation with historical data
Deployment architecture
- 1Machine signals collected in real time at the edge
- 2Time-series database stores health-relevant history
- 3Anomaly detection and trend models run on-premise
- 4Early warnings and reports reach the maintenance team
Integration
- Works on top of our data collection layer
- Alerts to email, dashboards, or existing CMMS systems
- Grafana and custom dashboards for maintenance teams
- On-premise deployment inside the factory network
Example use cases
- Robot condition monitoring from joint and cycle data
- Spindle and axis degradation trends on CNC machines
- Machine-to-machine comparison to find underperformers
- Alarm pattern analysis for recurring fault investigation
Interested in this solution?
Tell us about your machines and processes – we will propose a practical architecture and a pilot you can evaluate.
Discuss your project