How Amazon Lookout for Equipment works - Amazon Lookout for Equipment

Amazon Lookout for Equipment is no longer open to new customers. Existing customers can continue to use the service as normal. For capabilities similar to Amazon Lookout for Equipment see our blog post.

How Amazon Lookout for Equipment works

Amazon Lookout for Equipment uses machine learning to detect abnormal behavior in your equipment and identify potential failures. Each piece of industrial equipment is referred to as an industrial asset, or asset. To use Lookout for Equipment to monitor your asset, you do the following:

  1. Provide Lookout for Equipment with your asset's data. The data come from sensors that measure different features of your asset. For example, you could have one sensor that measures temperature and another that measures pressure.

  2. Train an anomaly detection model on the data.

  3. Monitor your asset with the model that you've trained.

You need to train a model for each of your assets because they each have their own data signatures. A data signature indicates the distinct behavior and characteristics of an individual asset. This signature depends on the age of the equipment, its operating environment, what sensors are installed (including process data), who operates it, and many other factors. You use Amazon Lookout for Equipment to build a custom ML model for each asset. For example, you would build a custom model for each of two assets of the same asset type, Pump 1 and Pump 2.

The model is trained to use data to establish a baseline for the asset. It's trained to know what constitutes normal behavior. As it monitors your equipment, it can identify abnormal behavior that might indicate a precursor to an asset failure. Amazon Lookout for Equipment uses machine learning to interpret the relationships between sensors, and to detect deviations from normal behavior because asset failures are rare and even the same failure type might have its own unique data pattern. Detected failures are preceded by behavior or conditions that fall out of the normal behavior of the equipment, and Lookout for Equipment is designed to look for those behaviors or conditions.

Additionally, if available, you can highlight abnormal equipment behavior using labels. The trained model can use the anomalous behavior in the dataset to improve its performance.

When you train a model, Amazon Lookout for Equipment evaluates how different types of ML models perform with your asset's data. It chooses the model that performs the best on the dataset to monitor your equipment.

You can now use the model to monitor your asset. You can also schedule the frequency with which Amazon Lookout for Equipment monitors the asset.