PI Asset Framework: Analytical Pros and Cons

OSIsoft’s PI System dominates the data historian market. A reason for the PI System’s success is its use of the Asset Framework (AF) feature. First introduced in 2007, AF provides PI users with an intuitive structure for organizing and using process data. With better organization PI users gain a greater sense of context for their data and the associated production processes.

While benefiting PI users, PI Asset Framework is also a boon for other automation solution providers like Control Station. Our PlantESP control loop performance monitoring (CLPM) solution benefits directly as configuration is accelerated dramatically whenever AF is in use. The addition of some PID controller attributes such as Controller Model, Enable Bits, among others may be needed for contextualization purposes. However, the majority of work is already done within an existing PI AF deployment.

The Asset Framework was designed to support a wide array of end-user applications including data analytics. Indeed, analytics can be calculated and the associated results can be visualized via PI Vision. However, a comparison of the analytical capabilities of the PI System and CLPM solutions like PlantESP reveals some important differences:

  • Sticking with Basics

PI AF is well-suited for assessing basic control loop performance attributes. More specifically, the PI System is limited to performing simple, arithmetic calculations. Those calculations are suitable for evaluating variation in a process signal and/or movement of a final control element such as a valve or damper. Common KPIs used to understand variation and movement include Average Absolute Error, Output Travel, and Output Reversals. 

While basic, the capabilities within PI AF shouldn’t be dismissed. KPIs that reveal fundamental performance attributes are an essential first step in any plant-wide optimization initiative.

  • Advancing to Optimization

In contrast, CLPM solutions are designed to address the full range of analytics from basic thru advanced. Beyond variation and movement, CLPM solutions are capable of recognizing complex patterns and behavioral attributes and of performing advanced forensics. Those are essential in conducting interaction analysis and isolating root-causes. Advanced KPIs and analytical capabilities found in select CLPM solutions include Oscillation, Modeling/Tuning, Correlation and Spectral Analysis.

The analytical capabilities found within CLPM solutions like PlantESP provide a natural ‘next step’ for users of PI Asset Framework as they advance through their analytics journey.

For obvious reasons the PI System equipped with Asset Framework dominates the market for data collection, storage, analysis, and visualization. Even so, other advanced analytic solutions like CLPM provide a valuable complement that enables manufacturers to achieve sustainable process improvement and plant-wide optimization.

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