A new way to think about control loops

Try this strategy for effective plant-wide loop performance monitoring and diagnostics.

Print page

Email page

By Alireza Haji-Valizadeh

PlantServices.com

Loop assessment, performance analysis and diagnostics have received considerable attention in recent years. Surveys have reported significant room for improvement in plant operations if an effective loop maintenance policy is adopted. One multi-industry loop audit survey reported that only about 32% of loops have acceptable or excellent performance. According to another survey, many industries are adopting some form of condition-based maintenance (CbM) as a predictive maintenance policy. Plant-wide maintenance practices have adapted to an ever-increasing demand for productivity. Industry needs a set of guidelines and strategies that leverage technological developments and maintenance productivity tools to increase overall production efficiency.

Loop assessment = predictive maintenance
Predictive maintenance requires using process diagnostics and performance analysis to focus maintenance resources on high-priority components with the greatest return on investment before they become intolerably faulty and chances for unscheduled shutdowns are high. Even though vendors and research academicians have developed indices to assess loop performance, questions remain:

• At what point does low performance indicate a problem?
• Which problematic loops provide the most room for improvement?
• For which problematic loops will maintenance maximize ROI?

The answers might alter one’s choice of predictive maintenance tools or assessment indices. Consider auto-correlation analysis of a process value to evaluate a loop, an assessment index that only can be evaluated or measured off-line. These activities usually need expert engineering, the most costly maintenance resource. Using engineers to evaluate loop performance using off-line software tools is a practice that could prove to be cost-prohibitive on a plant-wide basis. A key to predictive maintenance is identifying problematic loops offering the greatest ROI without using engineering effort. Industry has responded with online threshold and threshold violation schemes to identify problematic loops, efforts to define economical KPIs that can be related to loop performance indices and assigning priorities to the most critical loops.

But, evaluating and ranking certain high-level assessment indices for loops plant-wide can identify problematic loops that present the greatest maintenance ROI.

Loop as a controller
Traditionally, the term loop refers to a single controlled process, its feedback controller and related devices. Although it’s good to explore single-loop performance and to diagnose its faults, it’s not appropriate for plant-wide performance analysis. Predictive maintenance strategies should:

• Identify automatically the problematic loops that present the best opportunities for improvement on a plant-wide scale.
• Isolate loop faults with further tests and analysis.
• Provide remedies for faults and performance improvement.

Looking at loops as a cut on automation hierarchy and as a component in a plant-wide structure can help to devise an effective predictive maintenance strategy.

Loop as a cut of automation hierarchy
Most manufacturing plants are automated, and the system components can be organized into the following hierarchy:

• Main process.
• Device level (sensors, actuators and communication devices).
• Control level (algorithms such as PID, IMC and MPC).
• System level (automation programs in a PLC, DCS or PC).
• Supervisory level (HMI, recipe management, alarm management and optimization engines).
• User level (production operators and engineers).

This organization is based on the logical depth of system layers that fall between an operator – the highest level – and the underlying process of interest – the lowest level (Figure 1). For example, to introduce a change in the process, an operator first changes the setpoint field in an HMI level. This, in turn, changes some data located in the PLC, which translates into a setpoint change in a PID equation. Such a change, depending on the current value of the process variable, could introduce a controller output change that modifies a valve opening, which affects the process.

Figure 1. Automation Hierarchy

Click on image for a larger view