Predictive maintenance: forecast failures from sensor data and repair logs
We build predictive maintenance systems for plants, warehouses and fleets. The model reads sensor data, SCADA and repair logs. It warns of failure risk in advance, so you plan repairs instead of firefighting.
What hurts
A line stops without warning. While the team searches for the cause and waits for a part, a whole shift is idle and shipments slip. Emergency repairs cost more than planned ones, and customers charge penalties.
Maintenance follows the calendar. Parts are replaced while they could still run, or the moment they wore out is missed. The spare parts inventory grows, but breakdowns do not go away.
The data exists but nobody uses it. SCADA records vibration and temperature, while mechanics keep a paper notebook or a spreadsheet. Nobody has had time to connect the two.
What we build
- Equipment survey and selection of assets where failure costs the most
- Data collection from SCADA, PLCs, vibration and temperature sensors via OPC UA or MQTT
- Installation of extra sensors where existing ones are not enough
- Digitising repair and failure logs to train the model
- Anomaly detection and remaining useful life models per asset
- Alerts to mechanics in Microsoft Teams or Telegram explaining which signal changed
- An asset health dashboard in Grafana or your BI
- Links to your maintenance plan, CMMS or SAP PM and spare parts ordering
What you get
- A working data pipeline from equipment to storage
- Forecast models for selected assets with tuned thresholds
- An asset health dashboard and alert log
- Code, documentation and sensor wiring diagrams
- Training for mechanics and the chief engineer
How we measure the result
- Unplanned stops on the selected assets
- Share of failures the system warned about in advance
- False alarms per week
- Downtime hours and emergency repair spend
Systems and stack
Industries
Questions
How much does predictive maintenance cost?
From $15,000. The price depends on the number of assets, existing sensors and access to SCADA. If sensors are already in place and logging, the project is cheaper. If we need to install new ones and run network to the shop floor, it costs more. We fix the estimate after an on-site survey.
What data do you need?
Signals from equipment: vibration, temperature, current, pressure, run hours. And a repair log with dates and failure causes, even a paper one. Without failure history the model starts by detecting anomalies. Remaining life forecasts come later, as experience builds up.
Can it run without internet on site?
Yes. Processing can run on a local server at your plant, and data never leaves it. For industrial clients with strict requirements we deploy on dedicated servers. We work under ISO/IEC 27001:2022 and sign an NDA before access. EU AI Act and GDPR obligations are reviewed during discovery.
How accurate is it?
The model sometimes raises a false alarm or misses a rare failure. So we start with a few of the most expensive assets and tune thresholds together with your mechanics. Each alert is confirmed or rejected by a mechanic, and the model learns from that feedback.
How long does it take?
Between 8 and 14 weeks. Part of that time is data collection, because the model needs to see several operating cycles. First anomaly alerts usually appear mid-project. Remaining life forecasts get better with every month of operation.
How does the system grow after launch?
We extend the system to new assets and lines. Models are retrained when operating modes or equipment change. Under an SLA we monitor the data pipeline and sensors, so silence in alerts means normal operation, not a broken sensor.
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We reply within one business day. 30 minutes to tell you whether it is worth doing and what it costs.