Total: ~21 min

The Historian engine supports a variety of calculations, but pre-configured and customizable, that can be performed on top of the data you are already logging. For all practical purposes these new data sources behave like any others, and can be vital to the analysis performed in a variety of applications.

Using Tag Aggregates

Aggregates are a powerful calculation funtion that you can optionally add on to any data point being logged in the historian. Aggregates provide calculations over time, such as totalizers as may be used in energy analysis, OEE accumulations, SPC averages and standard deviations, to name a few. These aggregates can then be used in your application just like an ordinary data point.

Aggregate Groups

Aggregates are configurable to use a variable timeframe for calculation, which can be useful for calculations such as daily averages, running totals, and moving averages.

Aggregate Calculation Examples

There are a variety of pre-configured aggregate functions available to use. This video steps through some of the more commonly used aggregates, as well as how to get more detailed information on each.

Calculated Tags

The historian includes support for user-defined calculated tags. These calculations run atop the expression engine, including numerous custom functions available specifically to interface to historical data. These calculated or virtual tags can be used for any calculation, such as creating virtual energy meters, finding the maximum and minimums of zone temperatures, or the maximum and minimum of various cell productions to support MES cycle time analysis.

Calculation Triggers

Calculation triggers define the timing and interval for performing a calculation in the historian. They are necessary in order to configure a calculated tag to perform as needed.

Calculation Functions

Individual calculation functions can be parameterized, allowing you to leverage them quickly and at scale across your entire application. This standardization also helps to reduce user configuration error in repeated functions.

Performing Re-Calculations

Explain why you might do this (if you change your function, the calculation is defined after you already have logged data, or if you import data after the calculation has already run). Show a simple change and how the recalc tasks work, show the result. [Future] Audio levels are inconsistent, maybe related to trying to quiet mouse clicks. See at start of narration, 0:30, and 1:15

Compressing Stored Data

The Data Historian offers a feature that allows for data compression on an individual tag basis. This can be used to significantly reduce the overall storage space required for an enterprise level process data historian application.