The increased graphical abilities of software has resulted in automation vendors developing increasingly advanced graphics capabilities in their products. Scada/HMI visualisation today can be identified by the colourful graphics, sometimes even in 3D. Is this a good thing?
Introduction
It seems that most HMI graphics in control rooms are currently designed using the principles of: simulate the plant in as much detail as possible (so the operator can relate), and try to provide as much information on the HMI graphics as possible without cluttering it completely (it may be important). Unfortunately, this method of HMI design does not contribute to an increased ability to make effective decisions.
The advances of technology during the past two decades has resulted in plant control and information systems that bombard operators with thousands of alarms and events presented via sophisticated animated plant graphics. Although the graphics may look impressive, they flood the operator with too much information to process adequately, leading to mistakes or sub-optimal operations. The engineers that created these elaborate graphical displays historically did not always take into consideration human constraints and behaviour, and standards and best practices were not readily available to assist and guide the designers.
New research has indicated that a person can only process and react to a certain number of events at a time and that more information will become ‘noise’ or a distraction. This is apparent when one looks at a typical plant alarm log: thousands of alarms per hour, when an experienced operator can typically only react effectively to around six.
HMI graphics design must have a strong emphasis on the human element and specifically on decision-making within the operating environment. In addition, the effectiveness of the decisions is a function of experience, understanding and interpretation of production process conditions. This is not often realised and HMI graphics design (after all the clever thinking has been done) is left up to the most junior engineer on the team.
How do people decide?
Associative learning plays a significant role in building a person’s perception and opinion of a specific subject. The correctness of learning is influenced by the complexity of the system the person interacts with. The multiple dimensions, complexity and number of data-points for processes are in most cases too much for humans to comprehend. Consequently, humans look for patterns, simple rules and cause-effect relationships to reduce complexity.
Unfortunately, human minds do not all filter information in the same way, interpret the same information in the same way, or come to the same conclusion given the same information. It is also interesting to note that the possibility of misinterpreting a set of data will not deter a person from reaching a conclusion and making a decision. As such inaccuracy and bias are introduced into decision-making.
Information is defined as communicated or received useful knowledge concerning a particular fact or circumstance. This implies that the data describing the circumstance, event or condition has been interpreted, processed and presented in a useable form. This reasoning also implies that in the absence of a reliable interpretation, presented process data is nothing more than noise in the decision-making process.
It is therefore important to show not only process data, but to interpret and convert production process data into confirmed, validated and unbiased information. In other words, process decision-making is more than merely deciding what the set point should be for a flow rate or temperature. It is about the performance of the functional unit which may consist of various measurements. As most operators have limited process experience and little knowledge of design-principles, it is critical to make interpreted and unbiased information available if one wants to ensure effective decision-making.
Understanding what factors contribute to process variance, what the operational decision-making requirements are, and linking different operating conditions to specific performance levels provides a baseline to improve process (and KPI) performance through better decisions.
Improving plant level decision-making using real-time tools
To mitigate process complexity and reduce biased decision-making as much as possible, plant operators can be assisted by real-time tools to guide them in decision-making. These tools need to be simple and should provide actions and steps the operator can follow in the event that the process moves outside of the ideal state. The tools also need to be flexible so that the actions and steps and visualisation can be adjusted or calibrated easily by the process experts once new learning becomes apparent.
It is thus imperative that an HMI graphics standard should be defined that is based on unbiased information and proven relationships between KPIs and real-time process variables. Humans typically fail to detect gradual changes in process measurements especially if these changes take place over a long time period. The need thus exists to display information so that operators can clearly notice pertinent changes. Providing an automated monitor aiding operators to detect changes timely can prevent inefficiency and failures. The standards need to make use of simple graphics that immediately catch the eye when situations change (such as reduction in efficiency), or become safety critical, so that the appropriate actions can be taken.
The graphics need to be bland as people can easily be distracted by colour and movement. Flashing colours in abundance become distractions and may cause operators to lose focus. The simple principle for HMI graphics design should be ‘knowing what is going on around you’ with the objective of preventing abnormal or dangerous situations. The most effective visualisation tools are the ones that give us a broad system perspective according to the principles below.
Clarity
Graphics are easy to read and intuitively understandable (easy to see what’s going on).
* Graphics show the process state and conditions clearly (easy to see if the process is under control).
* Graphics do not contain unnecessary detail and clutter (less is more).
* Graphics convey relevant information, not just data (provide interpretation of information).
* Information has prominence based on relative importance (easy to see what is important).
* Indications of abnormal situations are clear, prominent and consistently distinguishable (easy to identify out-of-control, dangerous or sub-optimum conditions for instance using colour).
Consistency
* Graphic functions are standardised, intuitive, straightforward and involve minimum keystrokes or pointer manipulations (easy to react).
* The HMI is set up for navigation in a logical, hierarchical and performance oriented manner (easy to get around).
Feedback (interpreted)
* Graphic elements must behave and function consistently in all graphics and all situations (standardised interpretation and functions).
* Important actions with significant consequences will have confirmation mechanisms to avoid inadvertent activation (information interpretation and associated action guidance included in the HMI graphics).
* Design principles will be used to minimise user fatigue as graphics are used constantly (bland with only abnormal or dangerous conditions providing colour).
Benefits of new visualisation standards
Designing HMI graphics according to the principles above will provide the following benefits:
* Provide common, consistent use of colour (colour means something and is not for decoration – this immediately draws attention to indicate abnormal situations).
* Provide condition information (are we running in acceptable limits).
* Allow operators to see abnormal situations at a glance (shape or colour).
* Put information in context (provides the interpreted information).
* Interpret the process by putting data in context to reference values (e.g. alarm limits, past data, expected values, optimal limits).
* Use graphical tools such as bar-graphs and trends to give operator perspective and reduce their memory load (what is going on now vs what happened a while ago).
This allows operators to:
* Detect and react to abnormal or sub-optimal situations before alarms occur.
* Handle abnormal situations better by providing interpreted information and action guidance.
* Complete/resolve abnormal situation tasks faster by providing action steps and guidance.
The above once again highlight the need for confirmed, validated, unbiased and interpreted information that have clearly proved to be factors contributing to process efficiency increase or safety improvement. If the information is not properly validated and interpreted it will mean that operators will be wasting time reacting to events and variances in the process that will not affect efficiency or safety.
Conclusion
Today, leading technology providers have set standards and best practices, and provide tools to engineers that take into consideration the human constraints and behaviour through libraries and alarm management functionality. Making use of these tools can assist operations to improve plant operational excellence and safety by providing information to operators in a format that allow them to be effective and efficient. Alarming standards and best practices such as EEMUA 191 and ISA-18 are also available to guide and direct engineers and developers today.
Today, more than ever before, the global environment demands operations to be safe, sustainable and continuously striving towards operational excellence. The right technology partner can provide the tools to make this a reality on the plant floor.
For more information contact Gerhard Greeff, Bytes Universal Systems, +27 (0)82 654 0290, [email protected], www.bytessi.co.za
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