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The Anomaly Discovery Agent is especially useful for new assets, equipment with long life cycles, and unreachable assets such as deep-ocean or satellite systems where labels cannot be collected.

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Use Cases

  • Detect previously unknown equipment issues: The agent scores every window for how far it departs from normal, so problems you didn’t know to look for surface from the data alone.
  • Identify emerging process abnormalities: The agent gives every window a continuous anomaly score, so behavior drifting away from normal shows up as a rising score before it becomes an outright fault.
  • Surface novel failure modes: The agent attaches an anomaly score to every detection, so the most unusual windows can be surfaced first for a person to look into.
  • Monitor assets without labeled failure history: The agent builds its baseline from normal-operation data alone, so an asset with no recorded failures can be monitored from day one.

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See an Interactive Example

Try out the Anomaly Discovery agent with a predefined data set using our interactive example.

Anomaly Discovery: Bearing Monitoring Agent

Monitors bearings from a rig and detects when one fails because sensor output deviates from normal. No exemplar data is required.

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Using the Archetype AI Python Client

How to use the Archetype AI Python client to work with the Anomaly Discovery agent.

Anomaly Discovery Agent Example

An example that runs an Anomaly Discovery agent on input data using a canonical blueprint and a newly created bundle.