Predictive Maintenance with AI
We build predictive maintenance solutions that forecast machine failures before they happen, based on your sensor and maintenance data.
What is predictive maintenance?
Predictive maintenance uses operating data from machines to estimate when a failure is likely. Instead of servicing on a fixed schedule or after a breakdown, maintenance happens when the data shows it is needed.
Condition monitoring shows the current state and raises an alarm when a limit is crossed. Predictive maintenance goes further: machine learning recognises the patterns that usually come before a failure, often days or weeks in advance.
How the models predict failures
A model learns from historical data how vibration, temperature, pressure or power consumption changed before past failures. In operation it compares live values with those patterns and calculates a failure probability or remaining useful life. When the value crosses a threshold, a maintenance order is created automatically.
Data you need
- Operating data from PLCs, SCADA or IoT sensors.
- A history of faults, failures and maintenance work, ideally from your maintenance system.
- Asset master data: type, age and operating conditions.
For a first pilot the data you already collect is often enough. We check that in the first two weeks.
Off-the-shelf software or a custom solution?
Standard predictive maintenance software works well for common components such as motors, pumps and bearings. A custom solution pays off for special machinery, mixed equipment from several vendors, many data sources or when predictions must feed directly into existing planning systems.
Connected to SAP and your control systems
A prediction is only useful if it triggers action. We connect the model to your maintenance system, such as SAP PM, and create notifications automatically, so maintenance teams see predictions where they already work.
Frequently asked questions
What does predictive maintenance save?
Typically fewer unplanned stops and lower spare parts costs. We measure the effect in a pilot against your current maintenance strategy.
Do we need new sensors?
Not always. Existing data from control systems is often enough for a first pilot.
How long does a pilot take?
Eight to twelve weeks for one asset type, including data preparation and validation.
Predict failures before they cost you downtime
Send us a short description of your assets and data. We will tell you whether a pilot is possible with the data you already have.