Control Loop Performance Monitoring Overview

Process control is an engineering discipline for maintaining the output of a specific process within a desired range.

A control system is a device or set of devices used to manage the behavior of other devices or systems in an industrial process facility.

A control loop is the fundamental building block of industrial control systems. It consists of all the physical components and control functions necessary to automatically adjust the value of a measured Process Variable (PV) to equal the value of a desired Setpoint (SP).

A control loop includes the process sensor, the controller function, and the Final Control Element (FCE), all of which are required for automatic control.

The difference between the SP value and the current value of the PV is called the Error, and this is used by the control algorithm to calculate adjustments to the Manipulated Variable (MV). The MV is sent to the FCE (also referred to as the actuator) to bring the PV back to the SP value, using a closed loop feedback mechanism. This mechanism allows control systems to adapt to varying process operating circumstances and disturbances. Disturbances can sometimes be measured and compensated for by means of a Disturbance Variable (DV).

A PID control loop is a generic control loop feedback mechanism that uses a well-known PID(F) algorithm. It is very common in industrial control systems.

The PIDF (Proportional–Integral–Derivative-Filter) control algorithm involves four separate tuning constant parameters:

• Proportional value (P)

  • The variable control required in proportion to the current needs of the system.

• Integral value (I)
  • The integral control that applies ever-increasing control actions, until the error is reduced to zero.
• Derivative value (D)

  • The derivative value of the algorithm is determined by how quickly the error changes over time.

• Filter value (F)
  • Adding a filter (F) to the PID controller helps to remove noise signals from the system and enables better control under some conditions.

Many different implementations (variations) of the PID(F) algorithm are used in industry today.

Control Loop Performance Monitoring (CLPM) provides a solution to monitor the performance of an enterprise fleet of industrial process control loops, to detect and identify underperforming control loops, and to help to diagnose control loop problems.

Industrial process plants typically have hundreds or thousands of control loops configured inside PLC (Programmable Logic Controller), DCS (Distributed Control System), and SCADA (Supervisory Control and Data Acquisition) systems to automate and control plant operations.

Control Loops are located at the interface between equipment and process. They also operate industrial equipment to form an industrial process.

The stability, reliability, and optimal performance of an industrial process is dependent on the stability and performance of the fleet of control loops used to operate and control that process. Industrial process plants are also capital-intensive assets. Suboptimal control loops can directly impact equipment reliability, equipment lifetime, and operational performance.

It is therefore essential to manage the performance of control loops as valuable assets in an industrial enterprise facility that should be continuously monitored and optimized.

The asset model is central to CLPM in a number of ways:

• Loops are defined as assets, according to a predefined template/definition for a control loop.
• Deployment of data collections in Edge Analytics is driven by the asset model.
• CLPM configuration is driven from the asset.
• Loop thresholds and control limits are defined on the asset.
• Edge data is extracted from the asset.
• Actions on a control mode are defined on the asset.

Consult the Control Loop Asset Definition for details.

CLPM is made up of the following components working together.

These components enable the following functionality.

1. Loops are defined as assets, according to a predefined template for a control loop.
2. Selected tags are collected into TimeSeries for each loop.
3. Loop analytics are deployed.
4. Alerts produced by these analytics are triggered under conditions defined in analytic configuration.
5. Data produced by these analytics is viewed via the Fleet Dashboard and Loop Dashboard.
6. Issues identified with loops (cases) are resolved.

Following the CLPM workflow / process described below helps users monitor and optimize the performance of an enterprise fleet of control loops. Underperforming control loops can be identified, prioritized, and optimized.