Predictive Intelligence

Predictive Maintenance

Move from Reactive Maintenance to Predictive Decisions

A predictive maintenance solution that uses IoT data, analytics and AI to identify developing equipment issues before they cause unplanned downtime — supporting condition-based maintenance decisions.

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The Challenge

Why this matters

Traditional maintenance approaches — reactive (fix when broken) or time-based (scheduled regardless of condition) — both lead to inefficiency. Reactive maintenance causes costly unplanned downtime. Time-based maintenance wastes resources on equipment that doesn't need servicing. Neither approach uses the data the machines are already generating.

Key Capabilities

What Predictive Maintenance delivers

✓Machine health monitoring
✓Sensor data collection and analysis
✓Anomaly detection in equipment behaviour
✓Early warning alerts before failure
✓Maintenance scheduling support
✓Condition-based maintenance insights
✓Historical trending and reporting
How It Works

The Predictive Maintenance process

Predictive Maintenance connects machine data through IoT, applies analytics and AI to identify patterns that precede failures, and gives maintenance teams the information they need to act before problems occur — shifting from reactive to condition-based maintenance.

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01
Machines and assets connected through sensors
02
Real-time operational data collected continuously
03
Baseline operational patterns established
04
AI identifies deviations and developing anomalies
05
Early warning alerts generated for maintenance teams
06
Maintenance decisions supported by data rather than schedules
Business Benefits

What you gain

✓Reduction in unplanned downtime through early warning
✓More efficient use of maintenance resources and personnel
✓Shift from time-based to condition-based maintenance
✓Improved asset utilisation and operational availability
✓Data record for maintenance history and trending
Use Cases

Where it applies

◆Rotating equipment monitoring
◆Electrical and mechanical system health tracking
◆Production-critical asset monitoring
◆Multi-asset facility maintenance planning
Deployment
Integrates with existing IoT and operational data infrastructure. Edge and cloud options.
Integrations
Compatible with CMMS, ERP and operational data systems.
🔜 Future Enhancements
Advanced failure mode modelling. Multi-asset predictive analytics. Integration with maintenance work order systems.

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