The principle
Predictive maintenance forecasts impending failures instead of reacting on a schedule or only after the damage. At its core is anomaly detection: the system learns the normal, healthy behavior of a machine and reports deviations that point to an incipient defect.
How it works
- Capture: Real-time sensor data (e.g. vibration, temperature, current) from the equipment.
- Learn: A healthy reference profile is built from normal-operation data.
- Compare: New data is checked against the baseline; outliers are flagged.
Which methods
Anomaly-detection methods are commonly used — such as Isolation Forest, Local Outlier Factor or One-Class SVM, often combined to achieve reliable results. Detection frequently runs directly on the device (Edge AI) so that responses are fast and data-efficient.
The benefit
Less unplanned downtime, longer service life, plannable maintenance instead of costly emergencies.
Frequently Asked Questions
Do I need a lot of failure data for this?
Not necessarily. Anomaly detection mainly learns normal behavior — deviations from it are the signal. That is convenient, because real failures are (fortunately) rare and therefore scarce as training data.
Does this run in the cloud or on the device?
Both are possible. For fast response and data economy, detection at the edge is a good fit; for cross-device analysis, the cloud — often in combination.