Predictive Maintenance — before the machine stops.

Unplanned downtime is expensive. From machine and sensor data we detect wear and anomalies early — and because we understand the hardware ourselves, data acquisition sits in exactly the right place.

Discuss predictive maintenance

Sensor data
Anomaly detection
Remaining useful life
Dashboard
Edge or cloud

From machine to prediction.

Capture

Identify the relevant signals on the device and read them out reliably — often where we know the electronics ourselves.

Understand

Prepare the data, model patterns and normal behavior.

Predict

Detect anomalies and wear early, estimate remaining useful life.

Act

Alerts and key figures in the dashboard — maintenance that can be planned instead of reactive.

Frequently asked questions.

Do we need new sensors for this?

Often not — many machines already provide enough data. We first assess what is available and add sensors only where they deliver real added value.

Does this run in the cloud or on the device?

Both are possible. Time-critical or privacy-sensitive analysis can run directly on the device (see Edge AI), with aggregation and dashboards in the cloud.

Related services.

Reduce unplanned downtime?

Tell us about your equipment and data — we'll show you what can be predicted.

Meine
Harbke

info@samd-solutions.de
+49 1511 0988717

Harbke — Gesellschaftssitz & EMV-Labor mit Vollabsorberhalle · Am Glüsig 1C, 39365 Harbke

Meine — Technische Entwicklung (Elektronik & Embedded) · Peiner Straße 16, 38527 Meine

Mo–Fr 07:00–19:00 Uhr