Predictive Maintenance
Fix it before it fails.
Predictive maintenance that reads sensor data and maintenance history, estimates the remaining useful life of each asset, and tells your team what to service and when.
01 · Overview
Too early or too late
Calendar-based maintenance services healthy equipment too early and misses failing equipment entirely. Unplanned downtime hits production, safety and spare-parts budgets at once, while the sensor data that could have warned you sits unused.
Built with
- Time-series models
- Remaining useful life
- Edge deployment
Deployment
On-premise, private cloud or in-region.
02 · How it works
From sensors to work orders
- 1
Connect the signals
Vibration, temperature, pressure, acoustic and electrical data, plus operating conditions and maintenance logs, from SCADA, historians or IoT platforms.
- 2
Engineer the physics
Time- and frequency-domain features capture how equipment degrades.
- 3
Predict failure and life
Anomaly detection flags early deviation; survival and sequence models estimate failure probability and remaining useful life.
- 4
Plan the work
Predictions feed a schedule that balances risk, crew availability and spare parts, integrated with your CMMS.
- Sensors & SCADAVibration, heat, current
- Edge or stream ingestHistorians, IoT hubs
- Anomaly + RUL modelsRemaining useful life
- Risk-ranked work ordersCrew & spares aware
- CMMSTechnician feedback ↺
03 · Capabilities
Assets we monitor
- Rotating equipment: motors, pumps, compressors, turbines
- Wind and power generation assets
- Production-line machinery
- Vehicle and rail fleets
- Spare-parts demand planning
04 · Outcomes
What we measure
- Less unplanned downtime
- Unplanned downtime hours per asset
- Lower maintenance cost
- Spend per asset; avoided emergency repairs
- Longer asset life
- Mean time between failures
- Leaner spares
- Inventory value at equal service level
Example scenarios
Illustrative- Renewable energy operatorTurbine gearbox and bearing failure prediction.
- Automotive plantMaintenance scheduling across a production line.
- Logistics fleetVehicle component failure prediction.
- UtilityTransformer and pump-station health monitoring.
05 · Governance
Deployment & operations
Controls built in
- Edge or on-premise inference
- Integration with existing historians and CMMS
- Technician feedback loop
- Alerts explain which signals changed and by how much
Technology
- Python
- PyTorch
- Prophet
- statsmodels
- InfluxDB
- TimescaleDB
- Kafka
- Spark
- Grafana
06 · FAQ
Common questions
Do we need new sensors?
Often not. We start with the data your historian or SCADA already collects and recommend sensors only where they add value.
What if we have very few recorded failures?
We combine anomaly detection, which learns normal behaviour, with physics-based features, so the system is useful before many failures are recorded.
Can it run without a cloud connection?
Yes. Models can run on edge devices or servers inside the plant, with no data leaving the site.
Stop paying for surprises.
Start with one asset class and a back-test on your history.
Start a project
