Amazon Lookout for Equipment Documentation

Amazon Lookout for Equipment uses historical data and maintenance records (optional) from your existing machinery sensors to create a unique machine learning (ML) model for you to detect abnormal equipment behavior.

Time series data coming from sensors on industrial equipment can be highly erratic and the quality/usability of each sensor is difficult to determine. Amazon Lookout for Equipment can derive key statistics on ingested data from each sensor, grade the overall data quality and give a justification for its grade. This output helps a user determine which sensors are preferred inputs.

Amazon Lookout for Equipment is designed to leverage data from many different sensors, as well as maintenance history, in order to determine the optimal multi-variate model that best learns the normal behavior of the specified equipment.

For each detected abnormal behavior, Amazon Lookout for Equipment can assess the behavior and help a user identify which sensors produce unusual measures and what is happening in each of those sensors. Customers can use this information to diagnose the problem and take corrective action.

Deploy the developed model on real time data by setting up an inference scheduler. The scheduler can run inferencing on newly generated sensor data at user-defined intervals.

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