The Time Series Model (TSM) within Azure Time Series Insights Preview enables IoT architects to create custom models to contextualize IoT telemetry data. Using these models, end users can compute, correlate, explore, and analyze their IoT telemetry data to gain valuable business insights.
This article steps through high-level features and benefits of TSM, discusses its three components, and provides a quick tour of the models used in two different environments: a proof-of-concept smart campus for Microsoft's own corporate headquarters, and the Contoso Wind Farm demo environment.
Key features and benefits
Times Series Model data stored in Azure Time Series Insights Preview. Below are key features and benefits.
Components of Azure Time Series Model
After receiving an alert, an operator might come to the Azure Time Series Insights Preview explorer to get more data. For example, a damper “stuck closed fault” would go off if this damper is commanded open, but the sensor value indicates that it’s closed. This could indicate an obstruction of the damper, or a faulty motor. An operator might come to the explorer to get a visual of how long this has been an issue and to understand if this unit has experienced problems in the past. He or she could also use the level hierarchy to compare damper positions in other rooms to see if this is anomalous.
Notice that we can also view the underlying JSON code by clicking on “View JSON.” See Fig. 7 below.
Contoso Wind Farm
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