The Motion Condition Policy uses an algorithm to learn the normal vibration behavior of the asset on which the beacon is installed. Each beacon added to the policy will learn it’s own unique model. The length of the learning period is determined based on the configuration settings. After the learning period is complete, the policy will automatically go into deployment. In deployment, new data is evaluated against the learned model of normal vibration for each beacon. New data is tagged as “normal” if it falls within the bounds of the learned model and is tagged as “abnormal” if it falls outside the bounds of the learned model. An alert will be generated if the ratio of abnormal values exceeds a threshold over a given period of time. This threshold and time period are configurable.
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