On the surface, IAE appears a very good measure of performance. Indeed, for a controller humming along in automatic, this metric can provide some insight. But in a real-world process, many factors can directly affect it:
• During set-point changes, the PV may be far away from SP, leading to a false high value of IAE.
• More or fewer load upsets will change the value of IAE.
• When the loop is in manual, IAE has no real meaning as a measure of control performance.
For these reasons, only use IAE as a measure for “apples to apples” studies.
Harris Index. This measures performance as the ratio of the performance of the current controller to that of a Minimum Variance Controller (MVC). MVC is a theoretical controller that provides “best possible feedback control performance” — it tries to bring the PV back to SP in the shortest possible time by making relatively dramatic moves of the manipulated variable.
Unfortunately, real-world control valves can’t move 100% in one second! So we must settle for somewhat-slower-performing feedback loops.
The Harris Index offers some value a performance indicator. However, it has some restrictions:
• Calculations require an accurate estimate of process deadtime. It’s not always possible to have this in advance for thousands of control loops in a plant.
• Alone, it doesn’t account for the desired slowing of control loops to coordinate response with cascade, ratio or other loops.
In practice, the Harris Index works fairly well on fast control loops when you have a good estimate of deadtime.
Recommended Key Performance Indicators
Three particular performance indicators have delivered proven results over time:
1. service factor;
2. oscillation significance; and
3. valve travel.
Service factor. This is the percentage of time the control loop is fully in service — that is, operating in normal automatic mode with the control valve able to move and the PV within regular operating limits. In a well-performing loop, the service factor would be 100%. When it drops below 100%, start looking at each component of service factor individually — to see if you have a valve sizing problem, an instrument span problem or a tuning issue. Figure 1 shows a sample service factor report with many loops showing 0% of time in normal mode.
Oscillation significance. Plant efficiency, quality and overall performance can suffer because of significant oscillations, i.e., ones that measurably affect variability and have a distinct period of oscillation. So, it pays to determine the most important oscillations.
When you measure significant oscillations, you’ll find that many loops oscillate in tandem with others. So, when you can identify the root-cause oscillation, you can stabilize many loops with a single corrective action.
Determining root cause of oscillations involves detecting the period and strength of each oscillation. When oscillations propagate through a plant, they always stay at the same period. A report of oscillations sorted by period will help find root cause of each oscillation. In Figure 2, for instance, several loops are oscillating with a period of exactly 34.13 min. —a telltale sign there’s a single common cause to the oscillation of all these loops.