Intelligent decision support systems for nuclear power plants in Japan

Reliability Engineering and System Safety 22 (1988) 387-399

Intelligent Decision Support Systems for Nuclear Power Plants in Japan

Takamichi Ogino

Energy Science and Technology Department, Central Research Laboratory, Mitsubishi Electric Corporation,

l-1 Tsukaguchi-Honmachi 8-chome, Amagasaki, Hyogo, 661 Japan

Yasuo Nishizawa Energy Research Laboratory, Hitachi Ltd, 1168 Moriyama-cho, Hitachi-shi, 316 Japan

Toshihiko Morioka Isogo Engineering Center, Toshiba Corporation,

8 Sugita-cho, Isogo-ku, Yokohama-shi, 235 Japan

Norio Naito NAIG Nuclear Research Laboratory, Nippon Atomic Industry Group Co. Ltd,

4-1 Ukishima-cho, Kawasaki-shi, 210 Japan

Mamoru Tani Nuclear Systems Engineering Department, Mitsubishi Heavy Industries Ltd,

4-1 Shibakoen 2-chome, Minato-ku, Tokyo, 105 Japan

&

Yushi Fujita Electrical and Control Engineering Department,

Mitsubishi Atomic Power Industries Inc., 4-1 Shibakoen 2-chome, Minato-ku, Tokyo, 105 Japan

ABSTRACT

This paper describes the MITI (Ministry of International Trade and Industry) projects for decision support systems for NPPs in Japan. Main attention is paid to the new project of an advanced man-machine system for nuclear power plants, of which a conceptual design was initiated in 1984. Some of the aspects of the design and the outline of the prototype system are discussed.

387 Reliability Engineering and System Safety 0951-8320/88/$03'50 © 1988 Elsevier Applied Science Publishers Ltd, England. Printed in Great Britain

388 Takamichi Ogino et al.

1 INTRODUCTION

The Three Mile Island accident has markedly accelerated the development of operator support systems for nuclear power plants in Japan. The project of the Computerized Operator Support System (COSS), sponsored by government, started in the year after the accident. The main nuclear industries in Japan--Hitachi Ltd, Toshiba Corporation, Nippon Atomic Industry Group Co. Ltd, Mitsubishi Heavy Industries Ltd, Mitsubishi Electric Corporation and Mitsubishi Atomic Power Industries Inc.-- participated in the project. The design concept of the operator support functions and the methods to implement it have been established, and the prototype systems of COSS for PWR 1 and BWR 2'3 power plants have been developed.

Since implementation of the operator support system requires advanced technologies for information processing and man-machine interface devices, it has become widely recognized that the advancement of these technologies in the next several years will make it possible to enhance operator support system functions remarkably. Especially in order to establish flexible and user-friendly system functions, the system should use not only a great deal of knowledge regarding plant design and operational experience but also results obtained in ergonomics and cognitive science.

A new project of advanced Man-Machine System for Nuclear Power Plants (MMS-NPP) was initiated in 1984 to develop an advanced operator support system by applying a variety of innovative technologies mentioned above. The project is supported by the government financially and participants are from the same nuclear industries in Japan as in the preceding project. The first 3 years have been expended on conceptual design in which the targets and the scope of the project have been made clear and feasibility of the fundamental technologies, indispensable for attaining the targets, has been evaluated. The new stage of the prototype system development started this April.

MMS-NPP aims to support nuclear power plant operators in their various problem-solving activities by knowledge-based technology. Typical examples of problem solving are plant state diagnosis, operational goal selection and decision of corrective action under abnormal plant conditions. In the problem-solving activities, MMS-NPP provides relevant information to the operators by virtue of the knowledge bases, composed of both design phase knowledge and operational heuristics, and the inference mechanism.

Moreover, a machine problem solver such as MMS-NPP should be user- friendly in the decision-making process of nuclear power plant operators, so as to provide interactive capability for the operator as close as possible to his colleagues. Key technologies to realize the above-mentioned MMS-NPP

Intelligent decision support systems for NPPs in Japan 389

functions are knowledge representation and knowledge acquisition, inference mechanisms, and advanced man-machine communication technologies.

This paper deals with the main features of the MMS-NPP, which is composed of three systems, i.e. Knowledge Base Management System, Operating Method Decision System and Man-Machine Communication System.

2 KNOWLEDGE BASE MANAGEMENT SYSTEM 4

2.1 An approach to building knowledge bases

A Knowledge Base Management System (KBMS) supports engineers in efficiently building large knowledge bases to be used in MMS-NPP. Figure 1 shows a knowledge base building process.

Knowledge sources are classified into two kinds. One is heuristic knowledge utilized in the problem solving and is mainly based on engineers' know-how. Although the heuristic knowledge contains ambiguous information and occasionally lacks exactness, it includes knowledge

I Knowledge Representation

Knowledge Acquisition

Knowledge Verification

I Knowledge Compiling

Fig. 1. A knowledge base building process.


essential for problem solving. The other is so-called factual knowledge, and is described in design documents and drawings.

From the viewpoint of knowledge utilization, knowledge bases used in MMS-NPP are divided into two categories. One is used to identify the plant state, predict process behavior and decide the operator's corrective actions. The other is used to realize a user-friendly man-machine interface. The former includes various knowledges related to causal relations which are found in plant processes, plant system structures, system functions, plant operating procedures and planning, and equipment maintenance. The latter knowledge includes a plant domain model acting by an operator's cognitive processes, and knowledge related to verbal dialogues and graphics representation.

Knowledge representation should be flexible enough to describe the above-mentioned knowledge. Knowledge representation is dependent on knowledge type; a frame paradigm is suitable for the description of plant system structures, and causal relations between plant processes can be adequately described with a rule paradigm. Therefore a well-structured multi-type paradigm is necessary for knowledge representation in MMS- NPP.

Figure 2 shows a simplified example of a rule paradigm for identification of a leak from a Safety Relief Valve (SR/V) in Boiling Water Reactor (BWR) power plants.

Knowledge acquisition is one of the major problems in the development of knowledge base systems, and applicable technique is dependent on knowledge types. Factual knowledge related to plant systems' structures and their functions is acquired from various plant design data-bases. On the other hand, heuristic knowledge, e.g. causal relations between plant processes or plant operational know-how, is mainly obtained from domain experts in dialogue style with adequate knowledge acquisition support functions. The typical support functions for knowledge acquisition are as

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Fig. 2. A simplified example of rule paradigm for identification of a leak from a SR/V.


follows: (1) the heuristic knowledge from domain experts can be obtained and refined systematically by utilizing the domain models; (2) knowledge acquisition by a verbal protocol analysis is also an effective way in the circumstance of simulator experiments under abnormal plant conditions.

Knowledge verification is important to maintain the integrity of a knowledge base. It includes functions to find redundancy and incomplete- ness in the knowledge base, to verify consistency of the knowledge base and to examine the existence of deadlock during inference. Meta-knowledge is used as a constraint-type knowledge in the execution.

Knowledge compiling is necessary to enhance on-line inference performance. For instance, to accomplish high speed inference, common parts among rules stored in a knowledge base are compiled to transform them into networks. A case study experiment on the knowledge compiling has shown that the inference speed after the compiling was more than ten times faster than that without the compiling.

2.2 KBMS architecture

Figure 3 shows KBMS architecture considering the above-mentioned knowledge base building process.

The knowledge acquisition system is used to obtain heuristics from domain engineers and factual knowledge existing in plant system design documents and drawings.

The knowledge base verification system checks the acquired knowledge. As a result of the knowledge verification, large knowledge bases used in MMS-NPP are built.

Heuristics

Factual Knowledge

--~' Knowledge Base Management System I

Knowledge Knowledge 1 Knowledge Acquisition Verification System System I ~ Compiler

I On-Line I Knowledge Base

Fig. 3. Architecture of knowledge base management system.


The knowledge compiler transforms the verified knowledge into an on- line knowledge base so as to maintain the performance of the high speed processing, which is required for on-line inference.

Based upon the above consideration, the KBMS framework necessary for MMS-NPP is established and the essential technology for building the large knowledge bases of MMS-NPP are identified.

3 OPERATING METHOD DECISION SYSTEM

3.1 System functions

This system makes decisions regarding the plant operation. Its functions are comprehending plant conditions, deciding operating methods and procedures, and generating operator guidance information. Through these functions, the system supports normal operation, emergency operation and maintenance work of plants.

The functions are realized by an on-line inference in which plant data are used together with knowledge data about plant behavior and structure, characteristics of plant components, heuristics of operators, and so on. These data are accumulated and compiled in advance for on-line use through the knowledge base management system.

3.2 System architecture

Figure 4 shows the architecture of the operating method decision system. The system consists of an operating method management system, an operating procedure guidance system, a numerical simulation system and an on-line knowledge base.

The operating method management system is composed of a normal operation support system, an emergency operation support system, a maintenance work support system and an on-line inference system. The normal operation support system makes an optimum schedule for restart operations after reactor shutdown and load following operation, considering various conditions such as xenon distribution change and fuel burn-up, and monitors whether the reactor is being operated in accordance with the pre-determined schedule or not. The system also judges whether the plant is in a normal state or not by monitoring process parameters which change continuously during the load following operation. Further, this system supports inspection of plant components, surveillance tests and rotation of operating equipments. The emergency operation support system detects anomalies of the plant due to mis-operation, failure or abnormal behavior of


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the plant components, and investigates their causes. The maintenance work support system makes a schedule for maintenance tasks such as repairs of failed equipment, isolation of problem equipment from other parts of the plant, and decides manipulation procedures for the tasks. The operating procedure guidance system determines guidance information based on the outputs of the operating method management system. When numerical simulations are required in the process of the operating method decision, plant simulators and reactor core simulators included in the numerical simulation system are used.

Guidance information ~s transferred to the man-machine communication system and converted into user-friendly expressions for display or vocal announcement.

The on-line knowledge base required for the above-mentioned processes are stored in advance in memories by the knowledge base management system.

To assess the functions of the operating method decision system, a tentative system for emergency operation support was developed. ~ Simulations using this system have shown that it is possible to detect anomalies, to identify their causes and to provide suitable operation guidance information. Figure 5 gives an example ofa CRT display prepared for the feasibility study of the operating method decision system. The upper part displays process data. The lower left part shows messages dealing with

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inference results. The lower right part serves as an area for man-machine communications.

4 M A N - M A C H I N E C O M M U N I C A T I O N SYSTEM 6

4.1 System functions

This system adopts two approaches in order to realize a user-friendly man- machine communication system. One is to incorporate an operator model in the system to attain conformity between an operator's cognitive process and the information which MMS-NPP will provide for the operator. Under anomalous plant conditions, the operator imagines some scripts to recover proper conditions, based on the plant knowledge which he has acquired through training and operational experience. The system infers the operator's cognitive processes, corresponding to the above script, and the operator's focus of attention by use of an operator's model. Guidance information for the operator's decision making is arranged on the basis of the operator's focus of attention.

The other approach is to develop an advanced communicat ion environment for smooth dialogue between the operator and the system. Advanced man-machine devices to be used in the system include a voice recognition system, an audio response unit, high speed graphic display units with touch sensors and a large format display by which more than one operator can access the same information simultaneously. The voice recognition system permits the operator to use about a 1000-word vocabulary and to speak simple sentences continuously. Furthermore, the high speed graphic display system allows us to use understandable icons in pictures and to easily present information with a complex hierarchy through a window management technique.

4.2 System architecture

Figure 6 shows the architecture of the man-machine communication system. Input devices, such as a voice recognition system, a touch sensor and a keyboard, are systematically organized to form flexible and usable input channels. Although the usage of verbal inputs should be well examined due to the immaturi ty of the voice recognition technology, the recent advancement has given us an attractive possibility to put it into practical use. Queries from the operator through input devices are transmitted to the query understanding system which transforms the input information into an internal message form by use of the context of the dialogue and the plant


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conditions. Abbreviated expressions can be used when the context or the subject of the dialogue is clearly understood.

The message is given in the intelligent interface function (IIF). IIF uses an operator model which is composed of plant critical functions (PCF) models and a heuristic knowledge base obtained from plant operational experiences. The former includes hierarchically organized knowledge of plant functions which are derived from the plant design data-base. The operational goal, i.e. maintaining power operation or safety, is situated at the top of the PCF models, and the operational subgoals are developed below the goal. In the same way, the lower subgoals are broken down until they reach the physical component level. The PCF models are prepared according to the plant operational modes, i.e. modes of power operation, hot shutdown and cold shutdown. When an anomaly is detected, the IIF infers to which subset of the PCF models the operator's attention should be paid by use of the PCF's structural knowledge, heuristic knowledge base and symptoms of anomaly. In the following step, the IIF evaluates the status of the subset in cooperation with the operating method decision system in order to identify the subgoals to be most carefully observed, i.e. operator's focus of attention. Each subgoal has a cognitive index to evaluate its own status, relative importance in the PCF models, the relevant upper and lower subgoals, and so on. IIF identifies what information is potentially required in a given plant situation on the basis of the operator's focus of attention.

The guidance presentation system (GPS) represents the information from IIF in an understandable form using the media of voice and pictures. Display plays a main role for representing operational information, while

Fig. 7. An example of hierarchically structured picture.


the audio-response unit will be used for a supplementary purpose: to call the operator's attention to some events and to confirm the verbal inputs recognized by the unit.

Corresponding to each subgoal, a window picture is designed. In order to comprehend the situation of the subgoal and the location of the failure, simple and abstract symbols are used to design window pictures. One CRT picture is constructed by rule using several window pictures selected by the IIF on the basis of plant conditions. Figure 7 shows an example of a CRT picture which has been studied in the conceptual design phase. The window corresponding to the operator's focus of attention has the biggest space, while the relevant windows have smaller spaces. Windows in the upper left- hand side and in the back of the biggest one show the candidates for the operator's attention in the future and at present, respectively. Moreover, the operator can access the detailed relevant information through pointing icons with the shape of the file in the windows.

5 CONCLUSIONS

After a three-year conceptual design, including a feasibility study of fundamental technologies, for the advanced man-machine system for nuclear power plants, the development of the prototype system with the following features was started this year.

(1) Large-scale factual and heuristic knowledge base is systematically constructed by the best use of knowledge acquisition and verification technologies. An on-line knowledge base which is efficiently executable is automatically generated by use of a knowledge compiler.

(2) The operator support functions for the following situations are realized by balanced use of knowledge information and numerical simulation technologies: operation scheduling and monitoring, inspection management, emergency operation and maintenance work.

(3) User-friendly man-machine communication functions are established by incorporating an operator model and verbal and graphics-based dialogue.

REFERENCES

1. Masui, T., Tani, M. and Okamoto, Y. International Topical Meeting on Computer Applications for Nuclear Power Plant Operation and Control, The Development and Evaluation of Pressurized Water Reactor Advanced Control


Room Concepts in Japan, Part II: Computerized Operator Support System (COSS), Pasco, Washington, Sept. 1985.

2. Monta, K., Fukutomi, S., Itoh, M. and Tai, I. International Topical Meeting on Computer Applications for Nuclear Power Plant Operation and Control, Development of a Computerized Operator Support System for Boiling Water Reactor Power Plants, Pasco, Washington, Sept. 1985.

3. Higashikawa, Y., Murara, F., Hashimoto, S. and Kiguchi, T. International Topical Meeting on Computer Applications for Nuclear Power Plant Operation and Control, A Computerized Operator Support System for Boiling Water Reactor Abnormal Conditions, Pasco, Washington, Sept. 1985.

4. Naito, N., Tai, I., Morioka, T. and Kawakita, S. Annual Meeting of the Atomic Energy Society of Japan, Knowledge Acquisition and Management of Large Knowledge Base, Nagoya, April 1987 (in Japanese).

5. Nishizawa, Y., Shibata, Y., Kitaura, W. and Kato, K. Proc. 2nd Symposium on Human Interface, Development of a Knowledge Based Method of Plant Diagnosis and Operation Guidance, Tokyo, Oct. 1986 (in Japanese), pp. 179-84.

6. Ogino, T., Fujita, Y. and Morimoto, H. Seminar on Operating Procedures for Abnormal Conditions in Nuclear Power Plants, Intelligent Man-Machine Communication System for Nuclear Power Plants, IAEA-SR-123/21, Munich, June 1986.

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Intelligent decision support systems for nuclear power plants in Japan