Coding Schemes and Icons
Icons are powerful codes meant to be in-your-face obvious. This is the stuff that speeds up understanding and minimizes confusion. Icons both evoke a meaning and confirm that what is evoked is correct. While it might seem to be redundant, such redundancy is important.
Icons are more than placeholders and announcers of warnings. They also communicate status. Take icons like the thermometer-like icon shown in Figure 2. We call these context icons. They serve as indicator measurements of normalcy or pending abnormal operation. Looking at the left icon in the figure, note that all that is visible is the shape (a temperature), a range (the white vertical box) and the current measurement (the blue bar about halfway up the range). The displayed temperature is within its normal range. In the right icon, our temperature is abnormal. It is clearly too high. First, the normal range box now has all of its abnormal areas identified, from slightly abnormal to grossly so. We see that the current value, again a simple blue bar, is in a red area. Red means trouble. Something is clearly wrong here. To reinforce this message, the current actual value is displayed against a red background. It all suggests abnormal.
Now, let's put all these elements together. Illustrated in Figure 3 is a best-practice overview level display. Notice that all of the process elements are represented by neutral gray. This is what we expect. Process elements are placeholders. They're only there to show causal relationships and where things are okay or not. Arrangement conveys causality. Color is used for information only. Okay, there is the legacy video inset for the flare camera! It always needs to be in view.
Note the selection of key input variables at the left margin. These are "produced" by others at the enterprise. While not clearly depicted, the graphs of each input variable are contextualized in a manner similar to the icons. Their degree of normalcy significantly affects the production within our operator's area.
Next, we see the entire operator area of responsibility laid out with all key internal variables identified by context-bearing icons. Anything wrong will easily show up. Finally, observe the key product variables at the right margin. If these are okay and the process is okay, there should be confidence that the enterprise is okay. Where things are not, they show up directly and clearly.
More about HMI Best Practices
Corbett, D. Legg. Man Machine Interface. Litwin Process Automation, Houston, Texas, 1990.
"Effective Operator Display Guidelines," ASM Consortium, http://www.asmdashboard.com/, 2002.
Hollifield, B., D. Oliver, I. Nimmo and E. Habibi. The High Performance HMI Handbook. PAS. http://www.pas.com/. Houston Tex., 2008
"Human Machine Interface." NAMUR Standard AK 2.9. www.namur.de/ Potsdam, Germany.
"Process Plant Control Desks Utilising Human-Computer Interfaces–A Guide to Design, Operational and Human Interface Issues." Engineering Equipment Materials Users' Association (EEMUA) Publication 201. www.eemua.co.uk/ London, 2002.
Do ASM-Style Displays Work?
Sound like good ideas? Luckily, tragic incidents happen rarely, so very few plants that have employed the new designs have had one. With the new design, they are even less likely to see one at all. So we don't have actual case histories to demonstrate this. As a surrogate, carefully designed simulations were prepared and experienced operators used to work them. (See Errington, Jamie and Peter Bullemer, "Advanced Operator Interface," Human Centered Solutions, http://applyhcs.com/: 2008.)
The overall statistics from this test are very encouraging. In general, those using the ASM type displays were twice as fast in managing and almost 40% again more accurate. Even more revealing, those using the ASM displays were four times faster at early event detection.
By combining good human-factors engineering and years of experience with graphic displays, we've made significant strides forward from the big steps backward we made when we abandoned the panel wall. The plant operator gets the tools needed to do the job.
Dr. Rothenberg is principal of D-Roth Inc., www.d-roth.com, a subject matter expert in alarm management and operator interface development.
A Good Content and Design Example
A very good example of the ability to observe and understand complex interrelated situations can be illustrated by a deviation diagram, developed by the Foxboro Company for a product called VideoSpec. (See Figure 4, below). The order of the bars reflects the major production steps, process sequences, or processing order of the plant from material (or energy) entry into the operator area to exit. Time does not appear anywhere on the diagram.
The diagram is a representational construct of the whole of a plant and its relationship within itself and to its operational goals. It is constructed by first listing the major plant processed entities from entry at battery limits (from outside or another portion of the plant) to exit from battery limits. Adjacent bars mean that the entities they represent are functionally (if not physically) closely related in their processing steps. A bar to the left of another bar means that the one before comes before the one after in the normal "flow" of production. For each bar, the height shows how far away it is from a proper target. A "target" is not normally the related controller setpoint. Rather, the target represents the expected value of the particular attribute needed for long-term effective production. Right on target would be a bar of zero height. Values higher than target would be a bar height above zero, values below target would be a bar below zero.
Now we get to the part about what the diagram is intended to illustrate. Even if this is the first time ever looking at one, you can easily spot anywhere and everywhere there is abnormality in the plant. A single or isolated strongly deviating bar (at position 5 for example) means that something is clearly is "wrong," but its effect is strictly localized, for example, a failing transmitter that is not a part of a control loop. Groups of noticeably deviating bars (at positions 25 through 29 in our example) suggest that a broader area of the process is abnormal.
With a glance, the observer has a broad overview of the entire process, and if a good job was done selecting the variables to display, a good understanding of the overall process. It is a simple, yet powerful, visual agent! It's only one of many.