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annals of NUCLEAR ENERGY PERGAMON Annals of Nuclear Energy 26 (1999) 111 122 Knowledge acquisition for alarm processing systems of nuclear power plants using documents analysis Joo Hyun Park*, Poong Hyun Seong Korea AdvancedInstitute of Science and Technology, Department of Nuclear Engineering,373-1 Kusong-dong, Yusong-gu, Taejon, 305-701, South Korea Received 27 January 1998 Abstract The knowledge acquisition is one of the most difficult and time-consuming activities in developing knowledge-based systems. In this work, we propose a novel knowledge acquisition method through documents analysis. The knowledge base can be built correctly, rapidly, and partially automatically by using this method. This method is especially useful when it is diffi- cult to find domain experts. We apply this method to the knowledge acquisition for a simple dynamic alarm processing system (DAPS) for nuclear power plants and develop the alarm knowledge acquisition system (AKAS) for DAPS by using G2. © 1998 Elsevier Science Ltd. All rights reserved. 1. Intr6duction When transitions occur in large systems such as nuclear power plants (NPPs) or industrial process plants, it is often difficult to diagnose them. Various computer- based operator aiding systems have been developed in order to help operators diagnose the transitions of the plants. Operator aiding systems such as the fault diagnosis systems, the alarm processing systems, etc. are usually the knowledge- based systems (KBS) which use the knowledge either acquired from the domain experts or accumulated during plant operation. A construction procedure of the knowledge-based system is shown in Fig. 1. First, requirements of the knowledge-based system are obtained from users. Second, the knowledge is specified from the requirements. Third, the specified knowledge is * Corresponding author. Fax: + 82-42-869-3810. 0306-4549/99/S---seefront matter © 1998 ElsevierScienceLtd. All rights reserved PII: S0306-4549(98)00026-7

Knowledge acquisition for alarm processing systems of nuclear power plants using documents analysis

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Page 1: Knowledge acquisition for alarm processing systems of nuclear power plants using documents analysis

annals of NUCLEAR ENERGY

P E R G A M O N Annals of Nuclear Energy 26 (1999) 111 122

Knowledge acquisition for alarm processing systems of nuclear power plants using

documents analysis Joo Hyun Park*, Poong Hyun Seong

Korea Advanced Institute of Science and Technology, Department of Nuclear Engineering, 373-1 Kusong-dong, Yusong-gu, Taejon, 305-701, South Korea

Received 27 January 1998

Abstract

The knowledge acquisition is one of the most difficult and time-consuming activities in developing knowledge-based systems. In this work, we propose a novel knowledge acquisition method through documents analysis. The knowledge base can be built correctly, rapidly, and partially automatically by using this method. This method is especially useful when it is diffi- cult to find domain experts. We apply this method to the knowledge acquisition for a simple dynamic alarm processing system (DAPS) for nuclear power plants and develop the alarm knowledge acquisition system (AKAS) for DAPS by using G2. © 1998 Elsevier Science Ltd. All rights reserved.

1. Intr6duction

When transitions occur in large systems such as nuclear power plants (NPPs) or industrial process plants, it is often difficult to diagnose them. Various computer- based operator aiding systems have been developed in order to help operators diagnose the transitions of the plants. Operator aiding systems such as the fault diagnosis systems, the alarm processing systems, etc. are usually the knowledge- based systems (KBS) which use the knowledge either acquired from the domain experts or accumulated during plant operation.

A construction procedure of the knowledge-based system is shown in Fig. 1. First, requirements of the knowledge-based system are obtained from users. Second, the knowledge is specified from the requirements. Third, the specified knowledge is

* Corresponding author. Fax: + 82-42-869-3810.

0306-4549/99/S---see front matter © 1998 Elsevier Science Ltd. All rights reserved PII: S0306-4549(98)00026-7

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112 J.H. Park, P.H. Seong/Annals of Nuclear Energy 26 (1999) 111-122

Knowledge ......... ::::": ............... °~

Acquisition ~ i

Knowledgev and vBaSe I

Knowledge Basedl System V and V I

KBS Operation [

Fig. 1. A construction procedure of the knowledge-based system.

acquired from the domain experts or from the documents of the domain system. Fourth, the knowledge-based system is designed and constructed. Finally, the sys- tem is implemented in a computer program and operated.

In the above procedure, the knowledge acquisition and transfer process have gained the growing recognition of their significance (Gaines, 1987). Knowledge acquisition is the process of gathering knowledge about a domain, usually from experts, and transforming it into a computer program. KnOwledge acquisition is regarded as a bottleneck in the construction procedure of the knowledge-based sys- tems (Gruber and Cohen, 1987; Musen, 1989; Badiru et al., 1992).

Various knowledge acquisition methods have been developed by many researchers and the methods can be divided into two according to the source of the knowledge: one is to acquire knowledge from domain experts and the other is to acquire from documents analysis (Boose, 1985; Boose and Bradshaw, 1987; Klinker et al., 1987; Hwang and Tseng; 1990). The knowledge acquisition methods from the domain experts are such as direct interview, task performance and protocol analysis, ques- tionnaires and surveys, and so on. All of these are to acquire knowledge either through direct communication with domain experts or from observation of the experts performing tasks. Many domain experts are needed to build a good knowl- edge base using these methods. In industrial plants such as nuclear power plants, however, it is usually difficult to find domain experts available for the system devel-

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opment. There are many problems in communication between knowledge engineers and domain experts as well (Gaines and Shaw, 1993). Therefore, in many cases, the knowledge engineers acquire the knowledge of the knowledge-based systems through documents analysis. In this work, we develop a novel knowledge acquisition method through documents analysis. This method described in Section 2. We also apply this method to a knowledge acquisition for a dynamic alarm processing sys- tem in NPP and develop the alarm knowledge acquisition system (AKAS) by using G2, which are shown in Section 3 and Section 4.

2. Knowledge acquisition method through documents analysis: overview

The procedure for knowledge acquisition though documents analysis is decom- posed into four steps in this work--the problem definition step, the documents analysis step, the knowledge acquisition step, and the knowledge upgrade step as shown in Fig. 2. The knowledge attributes are determined in the problem definition step by analyzing the user requirements. The system is analyzed to acquire necessary knowledge in the documents analysis step. It is to note that some automatic knowl- edge acquisition methods can be developed using the relations between the knowl- edge attributes determined in the problem definition step and the domain system knowledge obtained in the documents analysis step: in the knowledge acquisition step, the knowledge attributes of the knowledge-based system are acquired auto- matically in the form of the prototype knowledge base using these automatic acquisition methods. The knowledge base is then investigated by domain experts to upgrade and validate the prototype knowledge base. These steps are further explained in the following sections.

2.1. Problem definition

A knowledge-based system to be developed is defined and the knowledge attributes and the rules are determined in the problem definition step: A knowledge engineer

Fig. 2. Knowledge acquisition procedure through documents analysis.

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114 J.H. Park, P.H. Seong/Annals of Nuclear Energy 26 (1999) 111-122

should schematically design the knowledge-based system from the user requirements determining what kind of knowledge-based system needs to be made, how to oper- ate it, what knowledge attributes are needed, how to operate them in the system, and so on. The problem definition step is composed of the following four sub-steps:

• Establishment of the top goal: The top goal of the knowledge-based system is established.

• Selection of methods: The methods to achieve the top goal are selected. All of the possible methods are described first and then the best method is selected from the described methods. More than one method can be selected if the knowledge-based system needs them to achieve the top goal.

• Analysis of functions: The selected method may have many functions to achieve the top goal. The functions need to be analyzed in detail to obtain the knowledge attributes.

• Determination of knowledge attributes: The knowledge attributes to satisfy the functions and the rules to operate the knowledge attributes are determined. It is recommended to decide the knowledge representation method in this step. The knowledge attributes acquired in this step are composed of general attri- butes such as name and location and functional attributes that are revealed through the analysis of the functionL

2.2. Analysis of documents

The purpose of the documents analysis is to understand and analyze the target system for knowledge acquisition. The documents analysis should be performed somewhat differently according to knowledge-based systems to be constructed. It is because the type of a knowledge base depends on the knowledge-based system even when the domain is the same.

It is not easy to analyze the domain knowledge with the attributes acquired in the problem definition step. It is because the documents do not show the information in the form of the knowledge attributes but show the information on the components and the sensors of the plant. The domain knowledge, therefore, needs to be obtained from the information on the components and the sensors of the plants.

The documents analysis step is divided into two sub-steps--understanding the domain knowledge and analyzing the documents. In the former step, the knowledge engineer defines the target system according to the user requirements, collects the documents, and arranges the collected documents. In the latter step, the knowledge engineer rearranges the documents for convenience in analysis, analyzes the docu- ments, and determines the methods to acquire the knowledge from the documents.

2.3. Automatic knowledge acquisition

One of main purposes of using computer tools for knowledge acquisition is to acquire the knowledge automatically. Automatic knowledge acquisition actually means the automatic generation of alarm knowledge attributes from the knowledge

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J.H. Park, P.H. Seong/ Annals of Nuclear Energy 26 (1999) 111-122 115

on components and sensors. The automatic knowledge acquisition methods are determined and practiced in this step. By using the methods, knowledge attributes of the knowledge base can be acquired from the analyzed knowledge on components and sensors obtained through the documents analysis. Since the automatic acquisi- tion methods vary according to the knowledge attributes, the knowledge engineer needs to determine the automatic acquisition methods for each knowledge attribute. Among knowledge attributes, some can be acquired directly from the analyzed knowledge and others can be acquired from comparison of objects in the analyzed knowledge. The automatic acquisition method, therefore, is dependent on not only the knowledge-based systems and but also the knowledge attributes. The knowledge is acquired automatically by using the determined automatic method and repre- sented to the form defined in the problem definition.

2.4. Upgrade of knowledge base through experts

Some of knowledge attributes may be acquired neither by automatic acquisition methods nor from documents. It is to note that the experts have the knowledge, through experience, which sometimes are not described in the documents. For this reason, the knowledge base which is built automatically needs to be reviewed by the experts who well know the system.

In order to upgrade the knowledge base by domain experts, we first make experts validate the procedures from the problem definition step to the automatic knowl- edge acquisition step. Second, we make experts supplement the knowledge base.

3. Knowledge acquisition for DAPS in NPP using documents analysis

Alarm annunciation systems in nuclear power plants alert the operators to chan- ges in the plant status. In general, operators use alarm information to monitor the plant, both from a safety perspective and from a power production perspective, interpret plant situations and take corrective actions. Conventional alarm systems, however, have some problems. During transients, for instance, the operators are overwhelmed by the numerous alarms of the plant. This is due to the cascading effect of the events, which can typically activate about 500 alarms during the first 5 s of a transient (Yang and Chang, 1991). Various studies on the alarm processing system of NPP have been performed since the Three Mile Island 2 (TMI-2) accident occurred in 1979.

The dynamic alarm processing systems (DAPS) have been developed by numerous institutes worldwide, which use the alarm processing techniques such as mode dependency, state dependency, level precursor, and cause--consequence relationship (Corsberg, 1987; Kim, 1994). The alarm knowledge must be acquired prior to use these processing techniques for the alarm processing systems. In this section, we apply the knowledge acquisition method developed in this work to knowledge acquisition for a simple dynamic alarm processing system which uses the above- mentioned processing techniques.

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3.1. Problem definition

• Establishment of the top goal: The top goal of the dynamic alarm processing system is to make the operator grasp the abnormal state of the plants correctly and rapidly through the dynamic processing of alarms in the NPP control room.

• Selection of methods: M 1: represent the alarms with a few priorities according to the" importance of alarms M2: process the alarms according to the sub- systems M3: process the alarms according to the functions M4: generate new alarms and so forth.

• Analysis of function: One or more methods can be selected according to the user requirements. In this section, all methods are considered to analyze the functions of the dynamic alarm processing system. Functions of M 1: process alarms according to the operation modes process alarms according to the state of components process alarms to use level precursor process alarms to use the cause-consequence relationship process alarms to use static priority remove the chattering alarms Function of M2: present the alarm according to the subsystems Function of M3: present the alarm according to the functions Functions of M4: endow the important parameters with a new pre-alarm set up a new alarm to the parameter or the component which does not have an alarm.

• Determination of knowledge attributes: The knowledge attributes of an alarm processing system should be determined in order that all of the functions are performed correctly in the system. The knowledge attributes are acquired through the documents analysis. The knowledge attributes determined to per- form the functions are mode, state, level precursor, causal alarm, static prior- ity, related subsystem, and related function, which are called functional attributes. In addition to the functional attributes, the knowledge has the gen- eral attributes such as name, location, message, source, setpoint, and so on. The alarm knowledge tables are made with these attributes as shown in Fig. 3. These knowledge tables are to be filled automatically with the knowledge acquired through the documents analysis and it is described further in the fol- lowing sections.

3.2. Documents analysis

The knowledge engineer determines the target system and defines the level of the analysis. The target system can be the primary system, the feedwater system, or the entire nuclear power plant. The level of the analysis is set up only for the purpose of acquiring the alarm knowledge. The documents related to the target systems are collected, which include process and instrumentation diagrams (P&ID), operation procedures, alarm guides, etc. When the documents are arranged in order to analyze the target systems, it is to survey the general characteristics of the alarms and then to classify the alarms by subsystems and characteristics before the documents analysis is performed. The alarm classification by subsystems is to divide the alarms into

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J.H. Park, P.H. Seong/Annals of Nuclear Energy 26 (1999) 111-122 117

Fig. 3. A blank table of alarm knowledge attributes.

groups that belong to the same subsystems such as reactor, pressurizer, and so on. The classification of alarms by the character is to divide the alarms into the state alarms and the process alarms. The state alarms are for the components such as pumps, valves, etc., and the process alarms for the processes such as flow, level, pressure, temperature, etc. The classification of alarms facilitates the knowledge engineer to analyze the target systems. The fluid flow and energy flow should also be understood to analyze the target system. The simple diagrams of flow, energy, and control help the knowledge engineer for this. The locations where alarms happen and the alarm setpoints are marked in the diagram. The arranged documents are rearranged to acquire the knowledge easily as follows:

• rearrange the diagram with sensors and components. • determine the operation modes (M) using the important parameters and obtain

the ranges of sensors (S) of each mode using simulators if needed. • represent the modes and sensors in the following forms in order to acquire the

knowledge. M = {ml, m2 . . . . . mn} Si = {Pml, Pro2 . . . . . Pmn}, Pmj = {Pmax, emin} where M, set of operation modes; mj operation mode j; Si, sensor j; Pmj, value of the sensor in the mode j; and emax(Pmin), maximum (minimum) of Pmj.

• select the alarms which belong to the state of the component. Ci : {SA1, SA2 . . . . . SAn}, SAy = {At, A2 . . . . . A,} where Ci, component i; SAj, state alarm j of the component; and Ak, alarm k which belongs to SAj.

• draw the causal-consequence diagram and obtain the causal objects of the components and the sensors from the diagram. cause of Si = {O1, 02 . . . . On}, cause of Ci : {O1, 02 . . . . . O n }

where Oi, causal object of sensor or component.

The complex causal-consequence relationships between alarms may be applied to the alarm processing system according to the user requirements, but usually the complex relationships are not needed to make the alarm processing system. The relationship, therefore, can be defined simply by the knowledge engineer.

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The knowledge attributes of the sensors and the components are obtained after the documents analysis, which are configured as follows:

• sensor = {name, type, alarm list, mode, normal value, causal object} type = {flow, pressure, temperature, level . . . . } alarm list = {(alarm name, setpoint)l, (alarm name, setpoint)2 . . . . } mode = {Pml, Pro2 . . . . . Pmn}, Pmj ---- {Pmax, Prnin} normal value = {Nml, Nm2 . . . . N, nn}, where Nmj =normal value of the sensor in the mode mj causal object = {Oi, 02 . . . . }

• component = {name, type, state alarm, causal object} type = {pump, valve, heater . . . . } state alarm = {SA1, SA2 . . . . }SAj = {Al, A2 . . .} causal object = {Ol, 02 . . . . }

3.3. Automatic knowledge acquisition

As the knowledge attributes of the sensors and components are obtained from the documents analysis, the methods that enable us to acquire the knowledge attributes of alarms automatically from those of the sensors and components should be determined in this step. In this work, we define four methods to automatically acquire the important attributes of the alarms such as mode, state, level precursor, and causal. The methods to automatically acquire other attributes can be defined additionally according to the knowledge-based system. The four automatic methods are shown as follows:

Mode:

select a mode among the set of M = {ml, m 2 . . . . }

select a sensor Si = { Pml , Pro2 . . . . . Pmn }, Pmj = {Pmax, Pmi.} select an alarm which belongs to the sensor alarm list = {(alarm name, setpoint)l, (alarm name, setpoint)2 . . . . } IF not Pmin of Pmj < setpoint of the selected alarm < Pmax of Pmj, T H E N mode j is related to the alarm.

State:

select a component which has the state alarms state alarm = {SAI, SA2 . . . . }SAj = {AI, A2 . . .} IF SAj ~ ~ , T H E N SAj is a state alarm of Aj.

Level precursor:

• select a sensor which has more than one alarms • select an alarm (Ai) among the alarms of the sensor

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J.H. Park, P.H. Seong/ Annals of Nuclear Energy 26 (1999) 111-122 119

IF (setpoint ofAi > setpoint ofAj > normal value) or (setpoint ofAi < setpoint ofAj < normal value), THEN Aj is the level precursor of A}. Where Ai, Aj : alarms which belong to the selected sensor (i ¢ j ) .

Causal:

• select a component ((7/) or a sensor (Si),

causal object = {Or, 02 . . . . }, O /= sensor or component IF causal object ~ ~ , THEN the alarms which belong to O} are the causal alarms of the alarms which belong to Ci or Si.

The alarm knowledge attributes related to mode, state, level precursor, and causal relationship can be acquired automatically using the above methods.

3.4. Upgrade of knowledge base through experts

The knowledge base of the alarm processing system should be validated by experts such as NPP operators who know the NPP alarm system well. To do this, first, the knowledge engineer makes the experts validate the diagrams of flow, energy, and control drawn from the documents to find any possible errors in the diagrams. The acquisition of the knowledge base must be repeated whenever the diagrams are improved by experts. In addition, the knowledge engineer makes the experts inspect and supplement the alarm knowledge. The knowledge base validated by experts in this manner is ready to be used in the alarm processing system.

4. Alarm knowledge acquisition system (AKAS) for DAPS

We have developed a knowledge acquisition system, AKAS, for DAPS by using G2. G2 is an expert system development tool, manufactured by Gensym, which is used for developing expert systems in many sites.

The target system is reactor coolant system (RCS) and its related systems of Kori NPP Unit 3 in Korea, which is described in Fig. 4. The system is composed of a reactor, three steam generators (S/G), three reactor coolant pumps (RCP), a pres- surizer (PRZ), a pressurizer relief tank (PRT), 34 sensors, and five valves. The knowledge on the target system was obtained from the documents analysis described in Section 3.2.

If a sensor or a component is selected on the screen, the knowledge table of it shows up as Fig. 5 describes. In this figure, the sensor knowledge table of PT455 is shown, which has attributes, such as name, alarm list and setpoint, operation mode, causal object, and normal value. There are two modes in this application--the mode 1 is the normal operation mode and the mode 2 is the reactor trip operation mode. A knowledge engineer is to fill in these sensor tables manually with the knowledge

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120 J.H. Park, P.H. Seong/Annals of Nuclear Energy 26 (1999) 111-122

ITo SV~

PRZ-

SG1

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LTq63

Fig. 4. A diagram of RCS and its related system.

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~R-~,.~ 167.72 prz-p,-hi 162.4 prz,-p-lo 153,6

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Fig. 5. A sensor table of PT 455.

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J.H. Park, P.H. Seong/Annals of Nuclear Energy 26 (1999) 111-122 121

$GI-Wt-HIH) SGI-WLD-HI SG1-WI_D-LO SGI-WL-LO

N N N N SG3-WL-HIH4 SG3-WLI)-N SG3-WLD-LO $G3-WI.-LO

N N N N RCS3-T-HI PP-Z-L-HI- A PRZ-L.HI PRZ-CC"L-HI

® N N N PRZ-P-LO-A PRZ-P-LC,-S~-A PRZ-V-T-HI PRZ L T H

N N N N PRT-T-HI PRZ-S-A*T tO PRZ-S-B-T-LO

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RCPZ-WA-HIHI RCP2-'Y'rA-H4 RCPZ-'v'TB-HIHI RCP2 ~ HI

NNN N RCPZ*UV RCP~-UF RCP3-UV RCP3-UF

Fig. 6. An automatically generated alarm knowledge table.

acquired through documents analysis. These filled tables become the input to the know- ledge acquisition system to fill in the blank alarm knowledge table shown in Fig. 3.

After knowledge tables of sensors and components are filled, action buttons on top of Fig. 5 are pressed in order to acquire alarm knowledge automatically. Action buttons make the procedure operate, which is constructed by using the methods in Section 3.3. For example, the action button named 'MODE' makes the procedure, mode-knowledge, operate. The action button named 'ALL' makes all procedure, such as mode, level precursor, causal, and state knowledge, operate.

Th~ alarm tables are automatically filled with the information that the procedures make, as shown in Fig. 6. In this figure, knowledge on the attributes such as name, source, setpoint, model, mode2, level precursor, causal alarm, and state is acquired automatically by the corresponding procedures. The rest attributes require specific knowledge of experts. For instance, the static attribute is determined in accordance to the knowledge which experts have obtained during the operation of NPPs. These attributes are acquired directly by experts. In AKAS, we put an opportunity that experts could write down the attributes on the alarm table.

The alarm knowledge acquired by AKAS can be used to dynamic alarm proces- sing system.

5. Summary and conclusions

A novel procedure and some methods to acquire the knowledge for the knowl- edge-based systems through documents analysis are developed in this work. The

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knowledge base of knowledge-based systems such as the alarm processing systems and the fault diagnosis systems can be constructed correctly, rapidly, and partially automatically by using these methods. The knowledge acquisition from domain experts usually requires a number of experts and it takes rather long time to develop the knowledge base. The method developed in this work, however, requires only one or two experts and relatively short time to develop the knowledge base. It is because the experts are involved only to validate and upgrade the knowledge base that has been already constructed partially automatically by the knowledge engineer through the documents analysis. The method is practically useful because the knowledge engineer acquires the knowledge not by interviewing with the domain experts who are often not available but by analyzing the domain documents.

Also, the alarm knowledge acquisition system (AKAS) for DAPS is developed by using these methods in this work. The alarm knowledge can be acquired easily from the knowledge of sensors and components. AKAS can be expanded to the full scope alarm knowledge acquisition systems of complex systems such as NPP without much effort.

References

Badiru, A.B. 1992. Expert Systems Applications in Engineering and Manufacturing. Prentice Hall, Eng- lewood Cliffs, NJ.

Boose, J.H., 1985. A knowledge acquisition program for expert systems based on personal construct psychology. Int. J Man-Machine Studies 23, 495-525.

Boose, J.H., Bradshaw, J. M., 1987. Expertise transfer and complex problems: using AQUINAS as a knowledge-acquisition workbench for knowledge-based system. Int. J Man-Machine Studies 26, 3-28.

Corsberg, D. 1987. Alarm filtering: practical control room upgrade using expert systems concepts. InTech 34, 39-42.

Gaines, B.R., 1987. An overview of knowledge-acquisition and transfer. Int. J. Man-Machine Studies 26, 453-472.

Gaines, B.R., Shaw, M.L.G., 1993. Eliciting knowledge and transferring it effectively to a knowledge-based system. IEEE Trans. on Knowledge and Data Engineering 50), 4-14.

Gruber, T.R., Cohen, P.R., 1987. Design for acquisition: principles of knowledge system design to facil- itate knowledge acquisition. Int. J. Man-Machine Studies 26, 143-159.

Hwang, G.J., Tseng, S.S., 1990. EMCUD: a knowledge acquisition method which captures embedded meanings under uncertainty. Int. J Man-Machine Studies 33,431--451.

Luger, G.F. and Stubblefield, W.A. 1993. Artificial Intelligence, Benjamin/Cummings, Redwood City, California, USA.

Kim, I.S., 1994. Computerized systems for on-line management of failures: a state-of-the-art discussion of alarm systems and diagnostic systems applied in the nuclear industry. Reliability Engineering and Sys- tem Safety 44, 279-295.

Klinker, G., Bentolial, J., Genetet, S., Grimes, M., McDermott, J., 1987. KNACK: report-driven knowl- edge acquisition. Int. J. Man-Machine Studies 26, 65-79.

Musen, M.A. 1989. Automated Generation of Model-Based Knowledge-Acquisition Tools. Morgan Kaufmann, San Mateo, CA.

Yang, J.O., Chang, S.H., 1991. An alarm processing system for nuclear power plant using artificial intel- ligence techniques. Nuclear Technology 95, 266-270.