System Model > Trends > View Trends

View Trends

Trend data is presented in trend graphs that visually represent past and current activity of plant-floor data, building a picture over time of how a variable (such as product output, level, temperature, and so on) changes or how a device or process is performing. You can monitor current activity as it happens and scroll back through time to view trend history.

Note: The Process Analyst (a built-in trend visualization tool) superseded the functionality of trend graphs. However, trend graphs are still supported. Be aware that SPC is not supported by the Process Analyst. To view SPC trends you need to use trend graphs. For details, see Process Analyst.

Trend Graphs

Trend graphs visually represent past and current activity of plant-floor data. As the values of variables change over time or as events happen, the graph moves across the page. The latest values are displayed by default. You can scroll back through historical data to display past values of the variable (or process).

You can trend any single variable or Cicode expression. You can display any number of trends on the screen simultaneously, even if they have different sample periods. You can also display up to eight trend tags (pens) in any trend window.

A trend graph can only communicate with one cluster, therefore you cannot mix trends from multiple clusters on a single trend graph. To graph trends from multiple clusters you will need to use multiple trend graphs, or, use the Process Analyst which has no such restrictions.

Historical data collection continues even when the display is not active. You can switch between pages without affecting trend graphs. Trend data acquisition and storage of data (in trend history files) continues even when the display is not active.

You can use the following standard trends:

Note: Variable tags can also be visually trended using an SPC Control Chart. Statistical Process Control (SPC) is a facility that enables you to control the quality of materials, manufactured products, services, etc. This quality control is achieved by collecting, arranging, analyzing, and testing sampled data in a manner that detects lack of uniformity or quality.

See Also

Published June 2018