Differences Between Model Types

The anomaly detection system offers three different types of model that can be used depending on how much data you have about your process.

You choose between these model types when creating a new anomaly detector.

The predictive model is the most powerful, but also the one that needs the most complete picture of your process.

This type of model builds a unique design of parallel neural network to try and predict what each asset property should be, based on the overall context of your asset.

However, this type of model may not work very well when you have changes caused by events or inputs outside what is 'visible' via the inputs to the AI model. If you aren't able to train an accurate version of this AI, we suggest then trying the Generative model.

This produces a value predicting model.

The generative model uses an autoencoder neural network to predict the overall balance of your asset properties based on the state of the system.

This type of AI model is less likely to indicate specific problems (ie. 'Tension Too Low'), but will still give indications of which parts of your asset are out-of-balance.

Because it focuses on the overall balance of inputs and outputs, it still provides useful indication of if a system is running normally.

This produces a value predicting model.

For simple applications - such as assets that don't have much context information explaining why they are doing what they do - Support Vector Machines provide a simple 'yes/no' signal showing if the asset is performing in an unusual way.

Unlike the other options, this type of AI isn't able to indicate what the problem is - just that the system is not working as expected.

This produces a outlier detection model.