Predictive Maintenance & Anomaly Detection

Your equipment already produces the data that announces failures – in controllers, SCADA and historians. We make it usable: anomaly detection and failure prediction without additional hardware.

  1. 01Uses existing data from PLC, SCADA, historian and MES
  2. 02Start without additional sensors or hardware
  3. 03Proof of concept in 6–8 weeks at a fixed price
  4. 04Runs on-premise or in your cloud
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From Raw Data to Maintenance Decisions

Models that learn from your historical operating data what normal looks like – and report when it no longer does

  1. 01

    Failure Prediction

    Models learn from time series and maintenance history which patterns precede failures and estimate the risk per asset.

  2. 02

    Anomaly Detection

    Detects deviations from normal behaviour in currents, pressures, temperatures or cycle times – even without labelled failures.

  3. 03

    Connected to Your OT

    OPC UA, S7, Modbus, MQTT, historian, SCADA, MES and maintenance system – read-only, the control system stays untouched.

  4. 04

    Alerts You Can Act On

    Warnings with context: which signal, since when, how unusual – sent straight to maintenance.

  5. 05

    Dashboards

    Asset condition, trends and forecasts for maintenance and production management at a glance.

  6. 06

    Fixed-Price PoC

    6–8 weeks: data access, a model on your historical data, validation against real events and a decision paper for the rollout.

Examples

Where It Applies

Anomaly detection on the data your equipment already delivers

Manufacturing Equipment

CNC machines, presses and conveyors: spindle load, motor currents and cycle times from the controller reveal wear early.

  • Rising spindle load announces tool or bearing wear
  • Deviating cycle times point to mechanical problems
Existing data
Data basis
6–8 wks
Proof of concept