A diagnostic expert system for the nuclear power plant based on the hybrid knowledge approach

IEEE TRANSACTIONS ON NUCLEAR SCIENCE, VOL. 36, NO. 6. DECEMBER 1989 2450

A Diagnostic Expert System for the Nuclear Power Plant Based on the Hybrid Knowledge Approach

JOON ON YANG AND SOON HEUNG CHANG

Abstmct- A diagnostic expert system, the hybrid knowledge based plant operation supporting system (HYPOSS), which has been developed to support operators’ decisionmaking during the transients of the nuclear power plant, is described. HYPOSS adopts the hybrid knowledge approach, which combines both shallow and deep knowledge to take advantage of the merits of both approaches. In HYPOSS, four types of knowledge are used according to the steps of diagnosis procedure. They are structural, functional, behavioral, and heuristic knowledge. The structural and functional knowledge is represented by three fundamental primitives and five types of functions, respectively. The behavioral knowledge is represented using constraints. The inference procedure is based on the human problem-solving behavior modeled in HYPOSS. The event-based operational guidelines are provided to the operator according to the diagnosed results. If the exact anomalies cannot be identified while some of the critical safety functions are challenged, the function-based operational guidelines are provided to the operator. For the validation of HYPOSS, several tests have been performed based on the data produced by a plant simulator. The results of validation studies showed a good applicability of HYPOSS to the anomaly diagnosis of nuclear power plant.

I. INTRODUCTION

HE NEED to use computers in nuclear power plants T (NPP’s) by the operating crew as an aid in making deci- sions had been widely endorsed in the numerous investigations following the Three Mile Island Nuclear Station Unit 2 (TMI- 2 ) accident in March 1979. The use of computers is expected to provide information analysis and integration of functions not achievable with conventional control room instrumenta- tion.

A post TMI-study concluded the following [l].

Time stress due to information overload and decision uncertainty increases the risk of error. Even with better training and improved procedures, human error can never be fully eliminated. Computer-assisted support systems are a promising technology innovation to aid knowledge-based decisionmaking by providing processed and derived information formed to assist the cognitive process, reducing information overload and thereby stress, reinforcing decision skills and thereby increasing confidence, and providing an overview of plant status to facilitate the prompt de- tection of inevitable errors.

Manuscript received May 26, 1989; revised June 30, 1989. The authors are with the Department of Nuclear Engineering, Korea Ad-

vanced Institute of Science and Technology, P. 0. Box 1560, Cheongryang, Seoul, Korea.

Manuscript Log Number 8930689.

A number of systems have been developed as computerized decision aids: alarm analysis systems, disturbance analysis systems (DAS), safety parameter display systems (SPDS), disturbance analysis and surveillance systems (DASS), alarm handling systems, and expert systems. The detailed contents of these systems are presented in many works [1]-[3].

Among them, artificial intelligence (AI) in the form of expert systems is being considered for a variety of operator support functions. Since the expert system REACTOR was proposed by Nelson in 1982 [4], a number of other expert systems have been developed for operational assistance [5]-[ 141.

In general, the knowledge used in expert systems can be divided into two types: shallow and deep knowledge. Shallow knowledge is the knowledge with no explicit representation of the underlying principles, e.g., cause-consequence tree, statistical result, and heuristics. Deep knowledge explicitly represents the underlying physical principles [lo].

Most current applications of diagnostic expert systems in electrical and medical domains are built based on shallow knowledge, e.g., rule-based approach. They utilize simple production rules to provide a mapping between possible faults and signals from systems. The result is an expert system with impressive capabilities within the area of expertise for which it has been prepared [ 151. Some diagnostic expert systems for NPP’s adopted this approach. RSAS developed by Sebo adopted a production rule-based approach for use at the NRC Operation center [9]. COPILOT developed by Kaplan used Bayes’ theorem to identify the cause of reactor trip [13], and Erdmann used a fault tree to build an expert system for safety diagnosis 1141.

However, most diagnostic expert systems for NPP’s utilize the deep knowledge approach [6]. This is due to the fact that the diagnosis of anomalies of NPP is different from that of the electrical or medical domain. The differences are as follows P I .

1) The system is very large and complex, i.e., the system consists of a large number of components, lines, valves, etc.

2 ) The system is dynamic, i.e., the observable signals are time dependent.

3) Many of the important signals are observable.

For instance, Yamada and Kiguchi attempted to use structural and causal knowledge for the accident diagnosis of NPP’s, respectively [SI, [7 ] . Nelson developed the response tree method used in REACTOR [4], [6]. Washio used semantic net to represent the structure of NPP’s [8]. Herbert applied

0018-9499/89/1200-2450$01 .OO O 1989 IEEE

YANG AND CHANG: DIAGNOSTIC EXPERT SYSTEM FOR NUCLEAR POWER PLANT 245 1

INFERENCE ENGINE

HYPO-cm H Y P o m * I I r I I I 1

I

E!!!!?- DESIGN

OPFXArnR

I Deep Shallow Guidelines

KNOWLEDGE BASES Fig. 1. Overall structure of HYPOSS.

a method of qualitative physics called IQA to the failure diagnosis of the pressurizer [ 101. Guarro implemented the logic flowgraph [12]. Also, some systems used a transient analysis code to simulate the consequence of generated hypotheses [ I l l .

However, when one tries to build a practical diagnosis system, several limitations of deep knowledge-based reasoning emerge. One is the fact that not all tasks needed for a practical system are easily accomplished with today’s understanding of the deep knowledge approach. Hence, a body of work is emerging on the appropriate coupling of deep knowledge of the system with shallow knowledge [15], [16].

In this consideration, we have developed an expert system, HYPOSS, to diagnose the anomalies of NPP’s and to offer correct operational response guidelines using a hybrid knowledge approach, which combines shallow and deep knowledge to account for the merits of both approaches. In HYPOSS, as in the IDM [16], at the initial stage of inference, shallow knowledge-based reasoning is applied. When the anomalies cannot be diagnosed by the shallow knowledge- based reasoning, deep knowledge-based reasoning is applied. Heuristic rules are used for shallow knowledge-based reasoning while structural, functional, and behavioral knowledge is used for deep knowledge-based reasoning. The combination of primitive- and constraint-based models is used to represent the deep knowledge. Rule-based deduction and abduction are used for shallow and deep knowledge-based reasoning, respectively, as inference strategies. Abduction is based on the human problem solving behavior in diagnosis tasks modeled in HYPOSS.

11. ORGANIZATION OF HYPOSS

The overall structure of HYPOSS is shown in Fig. 1. The system consists of one input processor, six knowledge bases,

and three data bases with an inference engine. The input processor transforms the numerical data from the NPP into sym- bolic form as used in HYPOSS. This processor, also calcu- lates the quantities such as enthalpy and the change of inventory, etc., which are not obtainable directly from the NPP, using the signals and design data of the NPP. The knowledge base is divided into three parts: the first one is for deep knowledge, the second is for shallow knowledge, and the last is for emergency operation guidelines. The title of each knowledge and data base implies its content. The detailed contents of knowledge bases will be presented in the following section. HYPOSS will operate on an IBM-PC AT Compatible using Prolog [ 171.

The control of inference execution and the display of diagnosed results can be made interactively through a CRT ter- minal. The form of the main display is shown in Fig. 2, in- cluding the overall system status and diagnosed results. The operational guidelines or detailed NPP states are given by pop- up windows at the request of operator. Operator and system interaction are facilitated by means of a menu provided by HYPOSS.

111. KNOWLEDGE BASE OF HYPOSS

A . Knowledge Base for Deep Knowledge There are three general types of information that deep

knowledge may provide: structural, functional, and behavioral. Structural knowledge consists of the physical relationships among the parts of a system, commonly called connectivity, and the manner in which the individual parts of a system are constructed. Functional knowledge is related to the idea of the intended structure, while behavioral or causal knowledge represents the knowledge of how the parts of a system behave [ 181. So, we have divided the knowledge base for deep

2452

Diagnosed Results

subcriticality : p n pressure of p n decreases core cwling : green due to pn-heater is down with CF heat sink : green integrity : green flow rate of pn-powline increases containment : green due to pn-porv-open with CF = inventory : green

flow rate of pn-sv_line increases due to DnSeoDen with CF = 0.5

IEEE TRANSACTIONS ON NUCLEAR SCIENCE, VOL. 36, NO. 6, DECEMBER 1989

Syatem System Item rcs power

P-m inventory fl0rFlte temperature

P-m inventory f l O W i 7 l t e temperature

scs power

slatus - Value state 100.68 steady

2183.25 decrease 247724.00 decrease

9419.55 steady 608.42 steady 0.00 steady

806.92 steady 124413.00 steady

519.47 steady

Type the one of options given in

I I Fig. 2. Typical example of main display.

knowledge into three parts. In this section, the principles to construct a deep knowledge base will be presented.

I ) Structure and Function Representation: A system manifests a certain behavior due to the manner in which the parts of the system are connected and the way in which the individual parts are constructed. The connectivity and function- ality of the system constitute some of the fundamental knowledge that a human acquires about all other knowledge for diagnosing the system. Regardless of the ultimate functional- ity of a system, most are based on a finite set of fundamental building blocks. These building blocks represent some general features that can be found within many different systems. Based on this notion of basic building blocks, a set of “fundamental primitives” was developed to describe to the computer how a specific system functions 1161.

The set of fundamental primitives for the structure description that we have implemented in our work is as follows: 1) components, 2) lines and 3) equipment.

1) Components are used to describe the passive objects that consists of the vessel with its related equipment such as the reactor, pressurizer, and steam generator, etc.

2) Lines are used to describe the connections between components.

3) Equipment represents the active objects such as various pumps, valves, heaters, etc.

Fig. 3 shows the typical forms of components and lines. Some examples of structure representation are shown below. The equipment representation is included in the component or line representation.

Typical examples of structure representation: component(level(2) ,class( 1) ,name(prz),

mass-in([prz-surge, prz-spray]) , mass-out( [prz-porv-line, prz-sv-line]) , energy-in( [prz-heater]) . energy-out(ll)).

line(level(2) ,class( 1) ,name(charging), from(rwt),

Component

-$- nout

€- Qout

Line

+4 wt 7- yam 1 PUMP HEATER VALVE 1

Fig. 3. Typical forms of component and h e .

through(pump( [chargingqump]) , valve( [charging-valve]) , heater([]))).

Functional knowledge is related to the idea of intended structure. It is developed from the consideration of why each part of the system was placed there by the designer. Such functional relationship may be necessary diagnosis anomalies since the number of system anomalies are represented as de- viations from intended structure 1181. To represent the functional knowledge, we have classified the functions for NPP’s into the following five types:

1) Power control 2) pressure control 3) inventory control 4) flow rate control 5) temperature control.

The other functions such as chemical control are not considered at the present stage. Typical examples of function representation are shown below. The structural and functional knowledge are represented using frames.

Typical examples of function representation: component~function(level(2) ,class( 1) ,name(prz),

power_control((+,[l>,(- 9 [I)), pressure-control(( +, [prz-heater]),

(-,[prz~spray,prz~porv~line,prz~sv~lineJ)), inventory-control((+,[charging]),( -,[let-down])), temperature-control((+,[]),(-,[]))).

line-function(level(2) ,class( 1) ,name(charging), temperature-control(( +, [I),(-, [I)), flow-control(( +, [charging-pump,charging-valve]),

(-,[I))). The schematic diagram of NPP’s considered in HYPOSS

is shown in Fig. 4. To treat the complex structure of NPP’s, the knowledge bases for structure and function have hierarchical structure according to the hierarchy of NPP modeled in HYPOSS, which is given in Table I. The level and class statements presented previously represent the status of representation in the hierarchy of knowledge bases.

2) Behavior Representation: The dynamic behavior of NPP can be represented by using governing equations: mass and energy balance equations, which are described as differ-

to(prz), ential or difference equations.

YANG AND CHANG: DIAGNOSTIC EXPERT SYSTEM FOR NUCLEAR POWER PLANT

TABLE I HIERARCHY OF NPP MODELED IN HYPOSS

Class

Level 1 2

secondary system 1 system RCS

2453

2 component reactor, PZR SG

line hot leg, cold leg, charging feedwater, steam line letdown PZR spray, auxiliary feedwater, PZR surge, SIS MSSV & ADV line

3 component relief tank, RWT CST

line PORV & PSV line

A b

I RCP

7 m y h - - . + + z F ; C ” C m

PIJw ( s m ) Fig. 4. Schematic diagram of NPP considered in HYPOSS. AFW = auxiliary feedwater; ADV = atmospheric dump valve;

BRS = boron recovery system; CST = condensate storage tank; CEA = control element assembly; MFW = main feedwater; MSSV = main steam safety valve; PH = pressurizer spray; PSV = primary safety valve; PZR = pressurizer; RCP = reactor coolant pump; RWT = refueling water tank; SDCS = shut down cooling system; SG = steam generator; SIS = safety injection system; SV = spray valve; and VCT = volume control tank.

In an expert system developed previously, the governing equations of a system are solved numerically using the assumptions given by the generated hypothesis, and the calcu- lated results are compared with the real plant data to confirm that hypothesis [ 111, while other systems attempted to simulate these equations qualitatively [ 101. However, these approaches have some defects. For the previous one, the time required to calculate the consequences of the several hypotheses can pre- vent performing the real time diagnosis. Also, the very conditions of most concern in analyzing the behavior of the system under abnormal conditions may violate the assumptions under which the simulation models were constructed in the first place [ 181. For the second one, at least in our experience, the qualitative physics has some limitations when simulating the overall behavior of the plant. Therefore, in HYPOSS, instead of simulating these equations quantitatively or qualitatively, the consistencies of the governing equations (which are represented by constraints) are checked at every sampling interval

using the signals from the plant. This procedure is similar to “the filtering for consistency with constraints,” which is used in the QSIM algorithm [19]. This is based on the fact that, as mentioned earlier, many important signals can be obtained from the various instruments of NPP. So, we have assumed that all signals of interest to the diagnosis can be obtained from the instruments of NPP or by simple calculations performed in the input processor.

The consistency check procedure is actually similar to the procedure a skilled operator follows in comparing function- ally related measurements for consistency with his “mental model” of how the plant behaves. Theoretically, this allows us to take full advantage of the known functional relationships between variables when processing the values of their measurements.

A governing equation can be written as

2454 IEEE TRANSACTIONS ON NUCLEAR SCIENCE, VOL. 36, NO. 6, DECEMBER 1989

where S1, Sz, . . . , S, = signals obtained from NPP or by calculation, and A = the residuals caused by the inconsistency of signals.

If the signals are consistent with the given governing equation, then A will become zero. Otherwise, A will not become zero and this can be used as the measure of inconsistency. Ac- cording to the amount and relative ratio of A, the degree of inconsistency is determined. Also, when a system breaks, A of the mass balance equation of the system can be used as the estimated value of flow rate through the broken area.

The forms of constraints for the mass balance check of a system is shown below. “Name” represents the name of the system. “CF” and “No” represent the degree of inconsistency and the number that the mass balance of the system is checked, respectively. “DMM” and “DMC” represent the inventory changes, which are obtained from measurement and by calculation.

Forms of constraints for mass balance check: mass-balance-check( Time,Name,CF,No,-) : -

component(level( Level),class(Class) ,name( Name), mass-in(WI), mass-out(WO), energy-in(QI), energy-out (QO)),

mass-summation(Time, WI, WIT), mass-summation(Time,WO,WOT), DMC is WIT-WOT, signal( Time, Name, inventory,_,-, DMM, State), mass-balance-state-check(Time,Name,DMC ,DMM.

CF,No). mass-balance-statePCheck( Time, Name,DMC ,DMM,

CF ,NO) : - DELTA is DMM-DMC, abs(DELTA,ADELTA), ratio-cacl(DMM,DMC,Ratio), cf-determination-for-mass( ADELTA,Ratio, CF l), retract(mass-balance( time(-) ,name( Name) ,cf( CF2),

check_no(Pno))) , CFM is 0.85*CF2, cf-summation( CF 1 ,CFM ,CF) , No is Pno + 1, assertz(mass-balance(time(Time),name,(Name),

cf(CF) ,check-no(No))).

To treat feedback effects of NPP’s, the state change due to operator or automatic protective actions that occurred during the transient should be reflected in the consistency check procedure. So, the occurrence of those actions is inspected and according to the inspected result, the change due to them is included in the consistency check process.

B. Knowledge Base for Shallow Knowledge

The knowledge base for shallow knowledge is divided into two parts. One is used as an independent subexpert system based on the shallow knowledge to diagnose typical or frequently occurring anomalies. In other words, through this part alone, some typical anomalies can be diagnosed which are im-

Emergency Situation

Function Restoration 1 1 -

Optimal Recovery (Om)

Interrupt-Return - I 1

Fig. 5 . Organization of ERG’s.

portant and have frequently occurred, such as loss of coolant accident (LOCA) or reactor trip. An expert system developed previously at the Korea Advanced Institute of Science and Technology (KAIST) [20] has been included in HYPOSS as a subexpert system based on shallow knowledge.

The other part of shallow knowledge plays a supplementary role for deep knowledge-based reasoning. The characteristics of a specific plant and the knowledge, such as the radiation effect or the complex structural relation, which is difficult to represent using deep knowledge, are embedded in this knowledge base in the form of heuristic rules. These rules are used to evaluate the generated hypotheses during deep knowledge- based reasoning. So, by improving this part, the performance of deep knowledge-based reasoning can be improved.

The typical representation of shallow knowledge is shown below. The example represents the case where an increase in the radiation dose of the steam generator blow down makes one expect that the steam generator tube will rupture.

Typical example of shallow knowledge representation: heuristics-evaluation(Time)-

retract(diagnosed-result(Time, sg_tube,break,CF)), signal(Time, sg-blow_down,radiation ,--)--)_, increase ,0) cf-summation(0.3 ,CF ,NCF) , assertz(diagnosed-result(Time,sg_tube,break,NCF)), fail.

C . Knowledge Base for Operational Response Guidelines The TMI accident has demonstrated that the guidance pro-

vided for mitigating the consequences of design-basis accidents could be inadequate when multiple incidents, failures, or errors occur simultaneously during or after the accident. In response to the U.S. Nuclear Regulatory Commission (NRC), Westinghouse and the Westinghouse Owner’s Group have developed new emergency response guidelines (ERG’s) [2 l].

The ERG’S are composed of two independent sets of procedures and a systematic tool to continuously evaluate the plant safety through the response to an accident. The overall organization of ERG’s is shown in Fig. 5.

I ) Optimal Recovery Guidelines (ORG 3): The ORG’s

YANG AND CHANG: DIAGNOSTIC EXPERT SYSTEM FOR NUCLEAR POWER PLANT 2455

are entered each time the reactor is tripped or the emergency core cooling system is actuated. An immediate verification of the automatic protective actuations is performed and the accident diagnosis process is initiated. When the nature of the accident is identified, the operator is transferred to the applicable recovery procedure and subprocedures.

2) Critical Safety Function Monitoring: Early in the course of the accident, the operating staff initiates monitoring of the critical safety functions. These are defined as the set of functions ensuring the integrity of the physical barriers against radioactive release.

3) Function Restoration Guidelines (FRG ’s): The FRG’s are entered when the critical safety function monitoring iden- tifies a challenge to one of the functions. Depending on the severity of the challenge, the transfer to FRG’s can be immediate for a severe challenge or delayed for a minor challenge. Those guidelines are independent of the scenario of the accident but based only on parameters and equipment availability.

Previous work has emphasized the function- or symptom- based operational aids such as FRG’s [2]. However, it should not be considered as a complete alternative to giving the plant operator an early and timely opportunity to revert the plant to normal operating conditions [ 121. The need also remains for complete diagnosis of anomalies in order to ensure that the adequate corrective action is taken [ 181. The knowledge base for operational response guidelines depends on the strategy of ERG’S. In HYPOSS, therefore, the event-based operational guidelines are provided to the operator according to the diagnosed results. If the exact anomalies cannot be diagnosed while some of the critical safety functions are challenged, the function-based operational guidelines are provided to the operator. However, the event-based guidelines are provided not only for the anomalies included in ORG’s but also for all anomalies that can be diagnosed by HYPOSS.

IV. INFERENCE STRATEGY OF HYPOSS

The inference strategy implies the method for linking the different knowledge types such as shown in Section 111-A and 111-B together to diagnose anomalies. In HYPOSS, as in the IDM [16], at the initial stage of inference, shallow knowledge- based reasoning is applied. If the anomalies cannot be diagnosed by shallow knowledge-based reasoning, then deep knowledge-based reasoning is applied. Rule-based deduction and abduction are used for the shallow and deep knowledge- based reasoning, respectively. In this section, only the abduction, i.e., hypotheses generation and evaluation procedures, will be presented since the shallow knowledge-based reasoning procedure was presented in the previous work [20]. The abduction is based on the human problem-solving model in diagnosis tasks that have been developed in HYPOSS.

Hypotheses are generated by three steps: overall system- state identification, classification of hypotheses categories, and possible cause generation. A complete description of the hypotheses generation procedure is shown below. The mass balance of a system is checked using the structural knowledge, and the constraints for the mass-balance equation over several time steps to mitigate the initial and/or delayed effect of a transient. The categories of hypotheses are classified into

“equipment malfunction” and “system break” according to the mass-balance state. At the present stage, the controller failure of equipment is regarded as the failure of equipment itself. The hypotheses-generation procedure allows use of a hierarchical description, a marked advantage for dealing with complex structure.

Hypotheses genera tion procedure: Step 1 : Overall System State Identification

1.1 Identify the system states for five parameters: power, pressure, inventory, flow rate and temperature.

1.2 Find the parameter which shows abnormal behavior.

Step 2: Classification of Hypotheses Categories 2.1 Check the consistency of mass balance equation. 2.2 Classify the categories of hypotheses into “equip-

ment malfunction” and “system break” according to mass balance state; If mass balance equation is consistent with signals of NPP, then the category of hypotheses is “equipment malfunction. ” Otherwise, “system break. ”

Step 3: Possible Cause Generation 3.1 For “equipment malfunction”:

Identify the related components, lines, and equipments, which can cause the identified system state. 0 For identified equipments, these become the

0 For identified components and lines, return to “equipment malfunction” hypotheses.

Step 2. 3.2 For “system break”:

The system whose mass balance equation is incon- sistent becomes the “system break” hypothesis.

The basic idea of this procedure is similar to the candidate generation via the constraint suspension technique, which is proposed by Davis [22]. However, the anomalies of the parts of NPP’s cannot be identified by simple relations between input and output as Davis did in the electrical circuit diagnosis. The function of the parts of NPP’s cannot be represented by such simple relations. So, we have represented the functional knowledge explicitly, and the equipment malfunction hypotheses are generated from this knowledge.

The generated hypotheses are also evaluated through three steps by backward chaining: evaluation by signals of equipment state, by system behavior, and by shallow knowledge. The overall hypotheses evaluation procedure is shown below. The certainty factor theory developed in MYCIN is adopted as the uncertainty management method [23]. The certainty factor of the hypotheses is changed according to the results of the evaluation procedure of the present time step and the values of previous one.

Hypotheses evaluation procedure: Step 1: Evaluation Using Signals of Equipment State

1.1 Verify automatic protective action. 1.2 Check the signal of equipment whether it is con-

sistent with a generated hypotheses. Step 2: Evaluation Using System Behavior


TABLE II EVENT SCENARIO OF PORV PRESSURIZER STUCK-OPEN

Time (S) Event Scenario

5 8

stuck-open pressurizer PORV with 100-percent capacity reactor trip by pressurizer low-pressure signal

18 HPI actuation

2.1 Check the consistency of energy balance equation under the assumptions given by the generated hypotheses.

Step 3:Evaluation Using Shallow Knowledge 3.1 This step is performed using the shallow knowl-

The overall efficiency of diagnosis can be im-

The metaknowledge is represented using rules, i.e., the hypotheses generation and evaluation are performed using rules. These rules access the structural, functional, and behavioral knowledge representations according to the need during the inference procedure.

V. VALIDATION STUDIES

The validation studies are performed for several cases. The data for validation studies are produced by a simulator developed in KAIST [24]. The validation of an expert system is to be performed for intermediate results, the final results, the reasoning of a system, or any combination of these three [25].

Here, small LOCA’s, due to stuck-open pressurizer power operated relief valve (PORV) like in the TMI-2 accident and due to a vessel leak of pressurizer, are diagnosed as examples of equipment malfunction and system break, respectively. These are also regarded as examples of anticipated and unanticipated anomalies, respectively. The validation studies of present works, are focused on the performance of deep knowledge-based reasoning since the results of the shallow knowledge-based reasoning were shown in [20].

In the validation studies, we have assumed that the signals from NPP (in this study, the data from the simulator) are vali- dated and multiple failures have not occurred simultaneously.

In the present stage, the data from the simulator to be diagnosed were prepared in an input file. This procedure is intended to simulate the on-line data transfer from NPP to HYPOSS.

A . Small LOCA Due to the Stuck-Open Pressurizer PORV

The event scenario of this validation study is shown in Table I1 and the diagnosed results are as follows.

The identified states of reactor coolant system (RCS) and secondary system (SCS) are shown below, where “power,” “pressure,” etc., represent the parameters the states which are to be identified. Two numerical values following these parameters represent the values and their change during a sampling time step; “steady, ” “increase, ” and “decrease” represent the identified states of the parameters. The last numerical value is the certainty factor for the identified state. “cf()” represents

edge which is explained in the previous section:

proved by this step.

the certainty factor of the mass-balance state and “check-no” is the number of times the mass-balance state is checked.

Identified system status (I): system-status(time(8) ,name(rcs),

status(power ,100.66,O. 1 18 ,steady,O. 992 188), (pressure,2 183.25, -55.109,decrease,0.984375), (inventory,247724, - 144,434, decrease ,O. 984375), (flowPrate,94 19.95, -3 1.556,steady,0.992188), (temperature ,606.42 1, -0.14 ,steady,O. 992 188))).

system-status(time(8) ,name)scs), status((power ,O,O ,steady,O .992 188),

(pressure, 806.922 ,O. 1 15, steady,0.992 1 88), (inventory, 1244 13, -0.243 ,steady,0.992 188), (flow-rate,O,O,steady,0.992 188), (temperature,5 19.472,0.017,steady,0.992 188))).

mass-balance(time(8) ,name(rcs) ,cf(O .0347098),

mass-balance(time(8) ,name(scs) ,cf(O) ,check_no(7))). check-no(7))).

As shown above, the mass balances of both systems are maintained, so the equipment malfunction is assumed. Then the states of components and lines related to RCS are identified and the surge line is selected as a candidate for further hypothesis generation, since only the flow rate of the surge line increases. The surge line has no equipment, so the change of the state of the surge line can be caused only by the change of the pressurizer. The identified states of the pressurizer are shown below. The mass balance of the pressurizer is also maintained so the equipment malfunction is assumed again.

Identified pressurizer status: component-status(time( 8),name(prz),

status( (power,O,O,steady,O.5), (pressure,2 179.87, -55.554,decrease,O), (inventory,22430.7,3.056,steady,O. 5), (temperature,54 1.366, -0.2 15 ,steady,O. 5))).

mass-balance(time( 8) ,name(prz) ,cf(O. 843374), check-no( 2))).

Finally, the results of diagnosis are shown. Two hypotheses are generated: the down of the pressurizer heater and the pressurizer PORV open. The certainty factor values resulting from the evaluation procedures show that PORV open is the most probable cause of a transient.

Diagnosed results (I): diagnosed-result(8 ,prz, pressure,decrease,prz-heater ,

diagnosed-result(8 ,prz-porv-line, flow-rate, increase,prz-porv,open,O. 875).

down,0.5).

YANG AND CHANG: DIAGNOSTIC EXPERT SYSTEM FOR NUCLEAR POWER PI.ANT 2457

B . Small LOCA Due to the Vessel Leak of the Pressurizer In this study, the data of the previous study are modified and

used, since the data for this study cannot be produced directly be the simulator. Here, it is assumed that the PORV is closed, i.e., the flow rate through PORV is zero and the loss of coolant by PORV open in the previous case is due to the vessel leak of the pressurizer. The diagnosed results are shown below. The diagnosis procedures are same as the previous case until the mass balance of the pressurizer is identified. In this case, the mass balance of the pressurizer is not satisfied. So its break becomes a hypothesis. The result of the evaluation procedure as shown confirms this hypothesis.

Identified system status (II): system-status(time( 13,name(rcs),

status( (power,9.2 13, -9 1.329,decrease,0.9995 12), (pressure, 1963.15, -275.209,decrease,0.9995 12), (inventory,249 129,1260.77 ,increase,0.998047), (flow-rate,9467,15.495, steady,0.999023), (temperature,597.809, -8.752, steady,0.999756))).

system-status(tirne( 13 ,narne(scs), status((power,O,O,steady,0.999756), (pressure,868.575,61.768,increase,0.9995 12), (inventory, 123859, -553.958 ,increase,0.998047), (flow_rate,O,O,steady,0.999756), (temperature,528.098,8.642, steady,0.999756))).

mass-balance(time( 13),name(rcs),cf(0.5 18671),

mass-balance(time( 13) ,name(scs) ,cf(O .4973 12), check-no( 12))).

mass-balance(time( 13) ,name(prz) ,cf(0.9 13642), check-no(7))).

Diagnosed results (11): diagnosed-result( 13 ,prz ,break,0.9784 1).

VI. CONCLUSION

An expert system, HYPOSS, has been developed to diagnose anomalies of NPP’s and to offer the correct operational response guidelines. This system adopts the hybrid-knowledge approach to couple the merits of the shallow and deep knowledge approaches. The results of validation studies show that the developed system can diagnose in principle all causes of anomalies if the structure and function description are adequate.

The merits of the hybrid knowledge approach can be found largely in four aspects.

The use of structural and functional knowledge provides closure and completeness. Closure is gained by using simple uniform inference mechanisms to derive a large number of possible faults directly from the description of the system instead of writing hundreds of rules. Com- pleteness is derived from examining all structure connections and paths so that nothing is forgotten. HYPOSS estimates and adjusts some parameters based on the signals from NPP’s to satisfy the consistencies o f the governing equations. Thus, the same hypothesis,

which shows different behaviors such as LOCA with different break sizes, can be managed in HYPOSS. Using shallow knowledge, the knowledge such as radiation effects, complex structural relations, or the special characteristics of a specific plant can be represented and used in deep knowledge-based reasoning to improve diagnosis efficiency. Also, the typical or frequently occurring anomalies are diagnosed effectively by an independent expert system based on shallow knowledge. Separation of deep knowledge types provides the high degree of flexibility for modification. Once the structure of NPP is changed, then only the modification of the knowledge bases for structure and function are required to reconstruct HYPOSS.

As the next stage of development, the knowledge base for operational guidance will be extended to give practical operation guidelines. The improvement of the consistency check method is required to treat system behavior more precisely. The module to explain the diagnosed result will also be developed.

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A diagnostic expert system for the nuclear power plant based on the hybrid knowledge approach