====AI Anomaly Detector Walkthrough====

To create an anomaly detector, sign in to ARDI as an administrator.

Then navigate to the dashboard of the asset you're interested in and click **AI Anomaly Detector**.

In our example, we'll choose **Bridle #4** from our **Paint Line Demo**.

===No Model Page===

Initially, we haven't got an AI model for this asset. To create one, we'll need to click **Create Model** to begin the process.

{{ai_step1.png}}

===Choose a Time-Span===

To be able to detect anomalies, the detector needs to see what //normal// actually looks like.

A time-range control will allow you to specify a range of time where your system was operating normally.

You'll be able to add additional examples of normal operation - particularly examples of your asset being offline or unavailable - after building your AI model.

===Choosing Points===

ARDI will nominate a set of possible points that might be useful when creating a predicative AI for the asset. 

{{ai_step2.png}}

You may notice that the system will select properties from a number of different assets. Anomaly detection AI works best when it has //context information// to work with - such as the environmental conditions, line speed/mode or settings of the system the asset is part of.

We suggest unchecking/ignoring any points that are at a dramatically different time-scale. For instance, remove any daily KPIs if most of the remaining points are live data points.

Once you've chosen which (if any) points to ignore, press **Build Model**.

===Wait===

Now you simply wait for the model to produce results. 

{{ai_step3.png}}

Once training is complete, you'll be able to press the **[[view_the_model_state|Click Here to View Model]]** page. 

===Adjustments & Tuning===

If your predicted values are too far off, you might want to [[training|add training data]]. 

If you find that values are being flagged as //anomalous// when they are still relatively close to the expected value, you might want to [[adjusting_tolerances|adjust your tolerances]].

You can also add additional data to your assets to improve the performance of your model.

These are discussed in detail in the section on [[twinengine:tuning|tuning your AI]].