ML20117F121

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Description of Westinghouse Operator Decision-Making Model & Function-Based Task Analysis Methodology
ML20117F121
Person / Time
Site: 05200003
Issue date: 07/31/1996
From: Reid J, Roth E
WESTINGHOUSE ELECTRIC COMPANY, DIV OF CBS CORP.
To:
Shared Package
ML20117F084 List:
References
WCAP-14695, NUDOCS 9609030322
Download: ML20117F121 (23)
v • d • e


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WESTINGHOUSE NON-PROPRIETARY CLASS 3 WCAP-14695 Description of the Westinghouse Operator Decision-Making Model and Function-Based Task Analysis Methodology by:

Emilie Roth Science and Technology Center July 1996 P

Approved:

R. M. Vijuk Wesunghouse Electric Corporanon Energy Systems Business Unit P.O. Box 355 Pittsburgh, PA 15230 4 % 5 O 1996 Wesunghouse EA% Corporanon M Rights Reserved WCAP-14695 JW 1995 rrum.wptit471596

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ment or parts hereof is prohibited. Nether this document nor any excerpts herefrom are to EPRI CONFIDENTIALITY / OBLIGATION CATEGORIES CATEGORY *A* -(See Dehvored Data) Consists of CONTP. ACTOR Fotoground Data that is contained in an issued reported.

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iii TABLE OF CONTENTS 1

I NTROD U CTION............................................... 1 - 1 2

OPERATOR COGNITIVE AND DECISION-MAKING ACTIVITIES............ 2-1 3

A FRAMEWORK FOR ANALYZING INFORMATION REQUIREMENTS FOR PLANT PROCESS MONITORING AND CONTROL.................. 3-1 3.1 FUNCTION DECOMPOSITION............................... 3-1 3.2 MODEL OF OPERATOR DECISION-MAKING.................... 3-4 4

FUNCTION-BASED TASK ANALYSIS: DETERMINING TASKS BY PLANT FU NCTION S...........,................................ 4-1 5

R E FE R E N C ES................................................ 5-1 b

WCAP-14695 JW 1996 m.m.wpt:1t471596

y LIST OF FIGURES Figure 3-1 Top Four Levels of the Normal Power Operation for a Westinghouse PWR....................................... 3-3 Figure 3-2 Top Four Levels of the Function Decomposition of Emergency Operation for a Westinghouse PWR.................................... 3 5 Figure 3-3 Rasmussen's Original Decision-Making Model from His Book.......... 3-6 Figure 3-4 Rasmussen's Model as Modified by D.D. Woods (includes the Groupings for Monitor, Plan, Control and Feedback)........................ 3-7 Figure 3-5 Structure of the Real-Time Analysis (From Woods, D.D. and Holinagel, E., 1 985)........................................ 3-8 WCAP-14695 July 1996 m.t'iO80er.W.1t> 071596

v UST OF ACRONYMS AND ABBREVIATIONS FBTA Function-Based Task Analysis HSI Human System Interface HFE Human Factors Engineering MCR Main Control Room PWR Pressurized Water Reactor RCS Reactor Coolant System WCAP-14695 4 1996 m.M.wptib4715M

I 11 1

INTRODUCTION One of the challenges for the human system interface (HSI) design team is to develop an HSI design that presents plant information arid controls in a useful, effective, and friendly manner.

To do this, the designers need to understand what decisions the operators are responsible for and how they go about making control decisions in real-time.

A premise of the HSI design is that errors of intention (incorrect or improper decision-making) can be reduced if the set of tasks that the HSI is designed to support, includes the cognitive activities that support operator decision-making.

This document describes the modeling framework and operator decision-making model used to help identify operator information and control requirements. This modeling framework provides the basis for the function-based task analyses (FBTAs) that are conducted as part of the AP600 human factors engineering (HFE) and HSI design process.

4 WCAP-14695 M 1996 m.m.wptib 071596

2-1 2

OPERATOR COGNITIVE AND DECISION-MAKING ACTIVITIES The AP600 HSI design is based upon the position that successful plant operation, from both a commercial and a safety perspective, is the responsibility of the main control room (MCR) operators and their immediate management. For the design of the AP600 HSI, a competent operator is defined as:

'one who is mentally ahead of the processes that he controls. Ideally, for this operator, there are no surprises.'

There are two characteristics that contribute to a successful operator. To be mentally ahead of a real-time process, the operator must have an excellent mental model or image of the behavior of that process. Plant processes are connected by more than just flanges and welds. These couplings are intrinsic in the nature of the processes involved, such as energy flow / transfer, chemical influences, relationships between voltage, Impedance, and current.

The operator must have an understanding of these relationships to be mentally prepared.

They become a mental model of the functionality of the process controlled. The operator, in real-time, maps the process variable values, changes in those values, and rates of change of those values, as provided by the process instrumentation onto that model. The operator interprets their meaning through the model (the current state of the process).

The second characteristic is that an operator who is mentally ahead of the process that he controls, questions and seeks validation of the information received. The operator is continually evaluating and validating the instrument indications to determine their accuracy. A competent operator does this in a number of different ways. For example, the operator uses his mental model to seek corroboration of a change in one part of the process with related changes in other parts of the process. The operator also uses this mental model to anticipate the consequences of control actions (initiated by either automation or by himself), and determines if those actions accomplish their purpose in process control situations, where much of the control action is initiated either by automatic control systems or is stimulated by operator procedures, the competent operator exercises skepticism regarding the effectiveness of the automatic action or the appropriateness of the procedure.

9 WCAP-14695 Jdy 1996 mA3088w.wpf;1b 071596

3-1 3

A FRAMEWORK FOR ANALYZING INFORMATION REQUIREMENTS FOR PLANT PROCESS MONITORING AND CONTROL A modeling framework helps the HSI design team identify the cognitive activities that need to be performed by operators. These cognitive operator activities work in concert with the dynamics of the AP600 plant processes and the supportive plant information and control requirements.

The modeling framework is based on the function goal-means decomposition, a model of human decision-making, and a cognitive task analysis methodology developed by Yens Rasmussen (Ref.1). The modeling framework has been adapted and extended by Westinghouse as described in References 2,3, and 4. It provides the basis for the FBTAs conducted as part of the HFE/HSI design process.

3.1 FUNCTION DECOMPOSITION The first element of the modeling framework is the development of a function decomposition representation of plant goals, functions, and processes. It is a representation of the process being controlled, expressed in terms of the functions or purposes that the designers intended their equipment design to accomplish. This function decomposition representation is also referred to as a goal-means structure.

A goal-means structure of the plant is used to provide a normative model of the plant processes controlled. The model provides representation of the physical plant and of the plant designer's intent. It takes the form of a graphic representation of the physical laws that govem the behavior of the process modeled. This representation is based on the concept of describing the plant's functional processes in terms of the goals to be achieved and the means or mechanisms available for achieving them. High-level goals, such as controlling primary coolant temperature, are accomplished by performing detail-level procedures, such as maintaining adequate power or providing heat removal capability from specific components.

The objective of performing this analysis is to develop a structure that links the purpose (s) of individual components or controllable entities with the overall purpose of the plant. This includes knowledge of the plant's physical structure, and the purposes or functions of the equipment. This representation organizes the knowledge that enables the HSI design team to identify and answer process control questions. The inputs to the goal-means modeling activity are the AP600 design documents containing plant information, such as the piping and instrumentation diagrams, system descriptions, and control and protection logic diagrams.

WCAP-14695 July 1996 m\\3088w.wptitK)71596

- ~ - - - - - -.- - -

3-2 l

The top four levels of the function decomposition for a Westinghouse pressurized water reactor (PWR), including the AP600, is shown in Figure 3-1. Lower levels of the representation become more design-specific.

The benefit of such a model to the design of the HSI, is that it sets the operating objectives and, therefore, defines the operators' cognitive tasks at the various levels of plant operation.

For example, if reactor coolant system (RCS) pressure needs to be reduced, this model shows that there is a connection between system pressure, temperature, and coolant mass inventory. The two cognitive tasks are choosing which one to work with and selecting the means (such as which valves to open or pumps to start) for effecting the desired changes.

This model also shows that there is a potential conflict in satisfying both the goals of the overall objectives of generating electricity and preventing radiation release. Similar conflicts show up at lower levels, such as the resulting conflict of the overall objectives or simply the result of the way that nature behaves. One may not, under certain circumstances, be able to satisfy or achieve the required multiple objectives simultaneously.

l This model, too, attempts to remain independent of the allocation of the cognitive tasks to man or automation. This model only maps the decision space required by the plant processes. It is a normative model since it defines normal conditions. Abnormality is defined as any state or set of conditions that is contrary to those described by the model.

The links that tend to be in a vertical direction, pass the requirements that the upper nodes 1

place upon the lower nodes. In this way, the " goals" pass their definition of satisfaction on to the "means' for accomplishment. These goals are expressed as predicates, that is, a binary, pass-fall statement of the process conditions that define satisfaction of the goal. With the appropriate language translation, such as, from " reactivity balance = 1.0" to " control rods at 215 steps and boron concentration = 1500 ppm" for the reactivity balance node as the goal, i

these predicates become the operating " targets" for the "means", (the equipment). One of the benefits of this model is that it shows, earfy in the human engineering design process, those operational conditions that have small or possibly imaginary operating envelopes if any exist.

Links that tend to be in a horizontal direction indicate that a commodity, such as temperature, pressure, energy, or fluid mass, " communicates' between, or flows from, one node to another.

The goal-means model in Figure 3-1 shows that under normal power operations, attention is directed to both the commercial electricity production and to plant safety. As noted in the model, these two goals are met by controlling the nuclear and thermodynamic processes.

The difference is in the targets and tolerable control bands that each goal demands, that is, in the predicates passed to the processes. From the perspective of the HSI design team, the mental model of the process that the AP600 HSI supports is the one that begins with the reactivity balance and ends with the environment. The difference between normal power

'l operation and emergency operation is that certain goals and their supporting means become WCAP-14695 July 1996 mM000w.wpt:1t>071596

3-3 d

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3-4 either ineffective, such as turbine control, or unavailable. Therefore, they are " cut away" in the model. This model makes apparent the logical, degradation of the goals. It also shows which process resources are available to achieve the goals as an abnormality or to make the processes deviate or degrade farther and farther from normal.

Figure 3-2 shows the model after a reactor trip. The consequences of process equipment degradation are reflected in the changing definition of goal satisfaction that is carried by the predicates. This model then, provides the HSi design team with the support it needs to design interfacing resources to be used by humans to accomplish a smooth, seamless transition from normal to emergency operations.

To complement and make effective those resources, this model of the processes needs to be the mental model possessed by the operators. Therefore, the mental model must be reinforced in the interfacing resources and taught in the training program.

The goal-means decomposition structures the plant's processes in such a way that the HSI designer can see why a control action is necessary (the goal statement as formulated by the predicates); what must be done to achieve the goal (s) (the choice of available attematives);

how the choices are to be implemented; and what control actions are necessary in order to achieve the goal using the chosen attemative(s) (the means) (Ref.1). This is a recursive hierarchy. That is, the required control actions themselves may become explanations of why other choices need to be made and other actions need to be taken, and so on. The model of decision-making that helps to further organize and structure the FBTA activities is described in the following section.

3.2 MODEL OF OPERATOR DECISION-MAKING A seccqd element of the modeling framework is a decision-making model that represents the cognitive activities of real-time process control operators. The modelis derived from Rasmussen's work (Ref.1). It has been modified by Westinghouse (Refs. 2,3) to clearly define the role of feedback in the decision-making process. Figure 3-3 shows the original Rasmussen model. Figure 3-4 is the Westinghouse-modified model(Refs. 2,3).

The modified model of operator decision-making (Figure 3-4), shows that Rasmussen's model can be further aggregated and abstracted to reduce it to the four, high-level cognitive tasks of monitoring, planning, controlling, and feedback. By mapping this model back onto the "why, what, how" hierarchy associated with the plant purpose or function decomposition model, the cognitive tasks that make up the operator's real-time, decision-making process can be more clearly understood as the continual task of seeking answers to a set of questions. A generic set of questions derived from Reference 3 is shown in Figure 3-5. Westinghouse has expanded and refined the set of questions for the purposes of FBTAs. The set of questions used in performing FBTA are listed in Section 4.

WCAP-14695 July 1996 m.i3088w.wpf:1b 071596

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WCAP-14695 4 3,,,

m.m.wpf:1b 071596 j

3-8

  • Goal /Means Structure Decision-Making Steps Monitor Planning Control P(x) - - - - - - - - - - - - - -
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WCAP-14695 July 1996 m.N.wP :1b-071596 f

3-9 In addition, the HSI designer considers the impact of automation. Automation changes the role of the operator and, therefore, the tasks for which the operator is responsible. The appropriate role change views the automation as part of the process being controlled. The operator becomes more of a systems manager, assessing the performance of the process under automatic control and deciding whether to intervene. The corresponding question in the FBTA deals with overriding automation: is intervent:on currently required?

Much of the moment to-moment operational activity is guided and constrained by procedures.

The impact on the cognitive Task Analysis is for the AP600 human engineering design team to realize that it is the responsibility of the operators to continually evaluate the operational success or failure of executing the current procedure. It is a fundamental assumption,in the design of the AP600 HSI, that the operators have a thorough understanding of the functional purpose or objective of each of the procedures. The operator understands what the procedure intends to accomplish, such as, cooling the core or improving the water mass inventory.

Finally, operators of real-time processes are continually plagued with the problem of data validity. An operator cognitive task is to continually assess the validity of the process data upon which the correctness of their decisions depends.

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WCAP 14695 July 1996 m:\\3088w.wpf:1t@71596

4-1 4

FUNCTION-BASED TASK ANALYSIS: DETERMINING TASKS BY PLANT FUNCTIONS

. nce the function decomposition structure is created, showing the relationships between gnis, processes, and attemative processes, then the FBTA can be performed. The objective af.he FBTA (sometimes also called a cognitive Task Analysis) is to determine the process plant data needed to support the decisions by a decision-maker, and to make the plant equipment achieve its designed purpose (Refs. 3,4,5). The Task Analysis involves superim-posing a set of questions derived from the decision-making model onto each node in the function decomposition structure. This defines the plant process data necessary to answer the questions. These data are then grouped to provide the requisite context. This FBTA represents plant systems and equipment control data requirements.

The operator decision-making model provides the basis for a set of questions and answers that define the data and control requirements needed to support real-time process control decision-making.

The FBTA maps these questions into each node of the function decomposition model and uses plant design data to 4dermine the answers. The HSI team creates the data that shows which plant process parami ters need to be measured to provide the decision-maker with the necessary data. Data reveals if the goal of a function is achieved or if the currently ope ating process equipment performs as the designers expect it to. The FBTA involves determining and documenting the answers to the following issues:

MONITORING / FEEDBACK I

Monitoring:

Data Validity - Is the process data valid?

Process Assessment - Where is the mass? Where is the energy? What is the reactivity level? Where is the radiation?

Feedback:

Goal Satisfaction - Is the goal being satisfied?

Process Performance - is the process that is currently deployed performing correctly?

Procedure Adequacy - is the current procedure achieving the desired purpose?

PLANNING Goal Selection - Which goal has the highest priority?

WCAP 14695 JJy 1996 mM088w.wpf:1t471596

4-2 Choices Among Attematives - Should a process be working?

Process Availability - Can an alternative process or subprocess by deployed?

Override Automation - Is intervention currently required?

Required Manual Actions - If intervention is required, what manual actions must be taken?

CONTROLLING Process initiation, Tuning, Termination - How is a process or subprocess controlled?

The FBTA is completed when data is created that contains the answers (such as what process data should be examined, what synthesizing technique or algorithm should be applied, or what system or piece of equipment (tag number) to execute actions upon) to the set of questions for each node in the function decomposition structure. The data is organized according to that structure and identi*ies the decisions and the cognitive tasks that the real-time processes require and the data necessary to make the decision (s).

The output of this exercise is a determination of:

What process data is necessary? What sensors are required? What are the required accuracies, and what algorithms are appropriate for combining sensor data into more abstract and meaningful data?

The relationships among the process data. The context in wh'ch each data element is presented to convey its meaning.

The relationships between data entities and the definition and organization of the plant process data base.

The physical actions that are taken to control the plant, whether by humans or automation.

When a task is automated, additional operator cognitive tasks are defined that need to be supported by information provided by the HSI. Typically, these added tasks deal with addressing such issues as whether or not the automatic system made the correct decision, whether or not to switch to MANUAL control from AUTOMATIC and, particularly in the case of the automatic protection systems, whether or not the full capability of the system is needed.

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For operations tasks not identified for automation, the HSI provides assistance to reduce operator mental workload in a number of other ways:

Synthesizing plant parameters from raw sensor outputs that are more representative of process performance Collecting and organizing related data in a way that minimizes the operator's data search for diagnosis and for other decision-making activities Accessing plant data from previous operational situations that have been stored in the system Providing graphic presentation of plant data to enhance the operators understanding of that data Collecting related control devices to create system level controls that more accurately parallel the operator's intention for a change in plant state.

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REFERENCES 1.

Rasmussen, J.,1986, "Information Processing and Human-Machine interaction, An Approach to Cognitive Engineering" (New York, North-Holland) 2.

Woods, D. D.,1982, " Application of Safety Parameter Display Evaluation Project to Design of Westinghouse SPDS,' Appendix E to " Emergency Response Facilities Design and V & V Process," WCAP-10170, submitted to the U.S. Nuclear Regulatory Commission in support of their review of the Westinghouse Generic Safety Parameter Display System (Non-Proprietary) (Pittsburgh, PA, Westinghouse Electric Corp.)

3.

Holinagel, E. and Woods, D. D.1983, " Cognitive Systems Engineering: New Wine in New Bottles," Intemational Joumal of Man-Machine Studies. Volume 18, pages 583-600 4.

Roth, E. and Mumaw, R.,1995,"Using Cognitive Task Analysis to Define Human interface Requirements for First-of-a-Kind Systems," Proceedings of the Human Factors and Ergonomics Society 39" Annual Meeting, San Diego, Ca., pp. 520-524 5.

Vicente, K. J.,1995, " Task Analysis, Cognitive Task Analysis, Cognitive Work Analysis:

What's the Difference?" Proceedings of the Human Factors and Ergonomics Society 39* Annual Meeting, San Diego, Ca., pp. 534-537 e

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