Sensor data that drifts before it fails.

Generate machine and sensor time series where readings hover around realistic baselines, drift before a fault, trip alarms and recover after maintenance.

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The example

A CNC machine heading for a vibration fault

What to look for in the screenshot above. These are the details that usually break in hand-made or random test data.

Readings hover around each machine's normal level, one row per minute.
Before the fault, vibration and temperature climb while RPM and OEE fall, together.
Status moves running → warning → alarm → E-STOP → maintenance, in order.
After the stop, RPM, pressure and cycle time drop to zero and temperature cools gradually.

Built for Manufacturing & IoT teams

Mean-reverting signals

Readings wander around a baseline instead of drifting to impossible values.

Failure patterns

Gradual drift, spikes and correlated changes before alarms.

Per-machine series

Every machine, line and sensor gets its own independent history.

Maintenance and OEE

Fault codes, work orders and OEE linked to the readings.

How it works

From idea to dataset in three steps

Describe or upload

Describe your data in plain English, or upload your SQL schema for PostgreSQL, MySQL / MariaDB, SQL Server and SQLite.

Review the plan

Check the tables, columns and rules the AI proposes, and adjust anything you like.

Generate and download

Choose how many rows you need and download a ready-to-run SQL script, CSV, Excel (one sheet per table) or JSON.

Questions

Can I generate second-level data?

Yes. Steps from milliseconds to years are supported.

Can I train anomaly detection on it?

Yes, for prototyping. Faults are labelled through fault codes and status.

Can I simulate many plants?

Yes. Each plant, line and machine gets its own series.