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In this research, lower shifted worth control rods (LSWCRs) are suggested to mitigate problems related to variation of axial power distribution during the power maneuvering of pressurized water rea...

In this paper, we propose an intuitive and practical method for system reliability analysis. Among the existing methods for system reliability analysis, reliability graph is particularly attractive due to its intuitiveness, even though it is not widely used for system reliability analysis. We provide an explanation for why it is not widely used, and propose a new method, named reliability graph with general gates, which is an extension of the conventional reliability graph. An evaluation method utilizing existing commercial or free software tools are also provided. We conclude that the proposed method is intuitive, easy-to-use, and practical while as powerful as fault tree analysis, which is currently the most widely used method for system reliability analysis.

Monitoring signals in the control room of nuclear power plants come in through multiple channels and are displayed as graphs on the display devices. It is crucial to make correct and reliable decisions on the relationships among the graphs, which are representations of signals from different channels, both by implemented algorithms and human decisions. Simplified schemes to compare signal envelopes using cross correlation and cross entropy are introduced. In this work, algorithms to curve-fit the signal envelope to a polynomial and envelope generation with interval means are also researched. When generating signal envelopes, using interval means instead of interval medians is safe enough and is not seriously influenced by possible outliers. An effective use of eigenvalues for verification of the signal correlation is pursued in this work for a three channel case.

In this article, a transient identification method based on a stochastic approach with the hidden Markov model (HMM) has been suggested and evaluated experimentally for the classification of nine types of transients in nuclear power plants (NPPs). A transient is defined as when a plant proceeds to an abnormal state from a normal state. Identification of the types of transients during an early accident stage in NPPs is crucial for proper action selection. The transient can be identified by its unique time-dependent patterns related to the principal variables. The HMM, a double-stochastic process, can be applied to transient identification that is a spatial and temporal classification problem under a statistical pattern-recognition framework. The trained HMM is created for each transient from a set of training data by the maximum-likelihood estimation method which uses a forward-backward algorithm and the Baum-Welch re-estimation algorithm. The transient identification is determined by calculating which model has the highest probability for given test data using the Viterbi algorithm. Several experimental tests have been performed with normalization methods, clustering algorithms, and a number of states in HMM. There are also a few experimental tests that have been performed, including superimposing random noise, adding systematic error, and adding untrained transients to verify its performance and robustness. The proposed real-time transient identification system has been proven to have many advantages, although there are still some problems that should be solved before applying it to an operating NPP. Further efforts are being made to improve the system performance and robustness in order to demonstrate reliability and accuracy to the required level. © 2002 Wiley Periodicals, Inc.

In order to increase computer system reliability, software fault tolerance methods have been adopted to some safety critical systems including nuclear power plants (NPPs). Prevention of software common mode failures is a crucial problem in software fault tolerance, but an effective method to solve this problem has not yet been found. Our research, to find an effective method to prevent software common mode failure s experimentally examined the impact of specification language diversity on NPP software diversity. Three specification languages were used to compose three requirements specifications, and programmers made 12 product codes from the specifications. From the product codes analysis using fault diversity criteria, we concluded that a diverse specification language method would enhance program diversity through diversification of requirements specification imperfections.

The purpose of this study is to suggest a framework to assess diagnosis error of operators in nuclear power plants. In nuclear power plants, human error caused by inappropriate performance due to inadequate diagnosis of situation by operators have been considered to be critical since it may lead serious problems. In order to identify and estimate the human errors, various human error analysis methods were developed so far. Most human error analysis methods estimate diagnosis error through time reliability curve or expert judgments. In this study, a new framework to assess diagnosis error was suggested. It is assumed that diagnosis error is caused by inadequate quality of data and diagnosis error can be observed by using information processing model of human operators. Based on this assumption, we derived the assessment items for the quality of data and diagnosis error taxonomy here.

Digital technologies have been applied in nuclear field to check task results, monitor events and accident, and transmit/receive data. The results of using these devices have proven that it provides high accuracy and convenience for workers to get obvious effects by reducing their work loads. In this work, as one step forward, the digital devices-based cooperation support system to aid communication between MCR operators and field workers in NPPs, Nuclear Cooperation Support and Mobile Documentation System (Nu-CoSMoD), is suggested. The suggested system is consist of the mobile based information storing system to supports field workers by providing various functions to make workers be more trustable for MCR operators, and the large screen based information sharing system to support meetings by sharing one medium to improve the efficiency of meetings. The usability was validated by interviewing field operators working in nuclear power plants and experts having experienced as an operator.

Recently, human error has been highlighted as one of the main causes of accidents in nuclear power plants (NPPs). In order to prevent human errors during the main control room (MCR) operations, which are highly complex and mentally taxing activities, improved interfaces and operator support systems have been developed for advanced MCRs. Although operator support systems have the capability to improve the safety and reliability of an NPP, inappropriate designs can have adverse effects on the system safety. Designs based on systematic development frames and validation/verification of the systems are pivotal strategies to circumvent the negative effects of operator support systems. In this paper, an integrated operator support system designed to aid the cognitive activities of operators as well as theoretical and experimental evaluation methods of operator support systems are reviewed. From this review, it was concluded that not only issues about systems (e.g., the accuracy of the system outputs),but also issues about human operators who use the systems (for instance, information quality, the operator’s trust and dependency on support systems) should be considered in the design of efficient operator support systems.

There are several methods for system reliability analysis such as reliability graphs, fault tree analyses, Markov chains, and Monte Carlo simulations. Among the existing methods, the reliability graphs are the most intuitive modeling method, but they are not widely used due to their limited expression power. In this paper, an intuitive and practical method for system reliability analysis named the reliability graph with general gates (RGGG) is reviewed. The proposed method introduces general gates to the conventional reliability graph method, which creates a one-to-one match from the actual structure of the system to the reliability graph of the system. A quantitative evaluation method is proposed by transforming the RGGG to an equivalent Bayesian network without losing the intuitiveness of the model. In addition, a method of analyzing the dynamic systems and repairable systems which uses the RGGG is introduced, and appropriate algorithms for the quantitative analyses are explained. It is concluded that the RGGG method is intuitive and easy-to-use in the analyses of static, dynamic, and repairable systems compared with other methods while its analysis results are the same as those of other methods.

We analyze the interactions between human operators and control room equipment and find that several factors affect the information-gathering and decision-making processes of operators: contents of the provided information, the way information is provided, and knowledge of the operators. These factors contribute to the perceived cognitive complexity by human operators in plant operation. Based on the information theory concept, we propose an integrated framework for evaluating this complexity. The proposed framework is designed to be applied to various types of control room equipment which have different types of man-machine interface and contain different types of information. An experimental verification for the proposed framework is performed and its result shows that the framework successfully integrates various aspects of man-machine interface systems and estimates the mental workload of human operators.

A fault diagnostic system (FDS) is an operator decision support system which is implemented both to increase NPP efficiency as well as to reduce human error and cognitive workload that may cause nuclear power plant (NPP) accidents. Evaluation is an indispensable activity in constructing a reliable FDS. We first define the dynamic aspects of fault diagnostic systems (FDSs) for evaluation in this work. The dynamic aspect is concerned with the way a FDS responds to input. Next, we present a hierarchical structure in the evaluation for the dynamic aspects of FDSs. Dynamic aspects include both what a FDS provides and how a FDS operates. We define the former as content and the latter as behavior. Content and behavior contain two elements and six elements in the lower hierarchies, respectively. Content is a criterion for evaluating the integrity of a FDS, the problem types which a FDS deals with, along with the level of information. Behavior contains robustness, understandability, timeliness, transparency, effectiveness, and communicativeness of FDSs. On the other hand, the static aspects are concerned with the hardware and the software of the system. For quantitative evaluation, the method used to gain and aggregate the priorities of the criteria in this work is the analytic hierarchy process (AHP). The criteria at the lowest level are quantified through simple numerical expressions and questionnaires developed in this work. these well describe the characteristics of the criteria and appropriately use subjective, empirical, and technical methods. Finally, in order to demonstrate the feasibility of our evaluation method, we have performed one case study for the fault diagnosis module of OASYS™ (On-Line Operator Aid SYStem for Nuclear Power Plant), which is an operator support system developed at the Korea Advanced Institute of Science and Technology (KAIST).

In the case of safety-related applications like nuclear instrumentation and control (NI&C), safety-oriented quality control is required. The objective of this paper is to present a software safety classification method as a safety-oriented quality control tool. Based on this method, we predict the risk (and thus safety) of software items that are at the core of NI&C systems. Then we classify the software items according to the degree of the risk. The method can be used earlier than at the detailed design phase. Furthermore, the method can also be used in all the development phases without major changes. The proposed method seeks to utilize the measures that can be obtained from the safety analysis and requirements analysis. Using the measures proved to be desirable in a few aspects. The authors have introduced fuzzy approximate reasoning to the classification method because experts' knowledge covers the vague frontiers between good quality and bad quality with linguistic uncertainty and fuzziness. Fuzzy Colored Petri Net (FCPN) is introduced in order to offer a formal framework for the classification method and facilitate the knowledge representation, modification, or verification. Through the proposed quality control method, high-quality NI&C systems can be developed effectively and used safely.

Core Protection Calculator System (CPCS) is a digital computer based safety system generating trip signals based on the calculation of departure from nucleate boiling ratio (DNBR) and local power density (LPD). Currently, CPCS uses ex-core detector signals for core power calculation and it has some uncertainties. In this work, a quantitative economic benefit assessment of using in-core neutron detector signals is carried out. In-core detector signals which directly measure the inside neutron flux of core are applied to CPCS to obtain more accurate power distribution profile, DNBR and LPD to reduce the calculation uncertainties. In order to improve axial power distribution calculation, piecewise cubic spline method is applied. Simulation is also carried out to verify its applicability to power distribution calculation in this work. Simulation result shows that the improved method reduces the calculational uncertainties significantly and it allows larger operational margin. It is also assured that no power reduction is required while Core Operating Limit Supervisory System (COLSS) is out-of-service when the improved method is applied.

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 difficult 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.

Development of effective measures for the complexity of human-computer interface design based on a proper model is desirable in order to improve the human performance and to facilitate the system development. In this paper, the authors propose the task-to-action (TTA) model which can describe the task-performing procedure of human operators. Quantitative interface design complexity measures using an informational entropy concept are also proposed to determine the user interface complexity, i.e., operation complexity, transition complexity, and screen complexity. Two experiments are performed in this work to show the validity of the proposed model and the measures: one is to test the validity of the TTA model with the case of information-seeking problem and the other for the suggested entropy measures.

The dynamic safety system (DSS) is a computer based reactor protection system that has dynamic self-testing feature and fail-safe nature inherently. The dynamic self-testing and fail-safe features provide a high level of reliability and low spurious trip rate. The time and human efforts maintaining the system can also be reduced by virtue of those features. In order to apply the DSS for PWR, however, some modification is required because there exist many differences between PWR and AGR for which the DSS was first tested and installed. These differences are trip algorithms, monitored parameters, trip logics, and other conditions. In this work, the DSS algorithm is modified for PWR. The modified DSS has several new features which are required for applying DSS technology to PWR. Then, the modified DSS is implemented in a programmable logic controller (PLC) and tested. It is shown that, from the results, the modified DSS generates correct output in all test cases.

In this work, software dependability under memory faults in the operational phase is predicted by two models: an analytic model and the stochastic activity network (SAN) model. The analytic model is based on the simple reliability theory and the graph theory, which represents the software as a graph composed of nodes and arcs. Through proper transformation, the graph can be reduced to a simple two-node graph from which software reliability can be derived. The SAN model permits the representation of concurrency, timeliness, fault tolerance, and degradable performance of the system and provides a means for determining the stochastic behavior of a software. Using these models, we predict the reliability of an application software in a digital system, Interposing Logic System (ILS), in a nuclear power plant and show the sensitivity of software reliability to major physical parameters which affect software failure in the normal operation phase. It is found that the effects of hardware faults on software failure should be considered for the accurate prediction of software dependability in the operation phase.

In this research, we propose a fuzzy gain scheduler (FGS) with an intelligent learning algorithm for a reactor control. In the proposed algorithm, the gradient descent method used in order to generate the rule bases of a fuzzy algorithm by learning. These rule bases are obtained by minimizing an objective function, which is called a performance cost function. The objective of the FGS with an intelligent learning algorithm is to generate adequate gains, which minimize the error of system. The proposed algorithm can reduce the time and efforts required for obtaining the fuzzy rules through the intelligent learning function. It is applied to reactor control of nuclear power plant (NPP), and the results are compared with those of a conventional PI controller with fixed gains. As a result, it is shown that the proposed algorithm is superior to the conventional PI controller.

In this paper, the self-organizing fuzzy logic controller is investigated for the water level control of a steam generator. In comparison with conventional fuzzy logic controllers, this controller performs the control task with no initial control rules; instead, it creates control rules and tunes input membership functions based on the performance criteria as the control behavior develops, and also modifies its control structure when uncertain disturbance is suspected. Selected tuning parameters of the self-organizing fuzzy logic controller are updated on-line in the learning algorithm, by a gradient descent method. This control algorithm is applied to the water level control of a steam generator model developed by Irving et al. The computer simulation results confirm the good performance of this control algorithm for all power ranges. This control algorithm can be expected to be used for the automatic control of a feedwater control system in a nuclear power plant with digital instrumentation and control systems.

In this paper we develop and present an improved resistive circuit fault diagnosis method, a type of simulation after test (SAT), for the location of multiple faults in linear network. In this method we suggest a condition of branch fault diagnosis for locating multiple faults which is based on multiple excitation. Our diagnostic method based on this condition has the major advantage that it removes the necessity to verify the existence of multiple fault combinations. We demonstrate this method by applying it to a resistive network.

It is investigated in this paper that how learning algorithm of fuzzy system can be arranged by gradient descent method and how the learning speed can be increased in this method. First, the optimal range of learning speed coefficient not to be trapped in local minima and not to provide too slow learning speed is investigated. With the optimal range of learning speed coefficient, the optimal value of learning speed coefficient is suggested. With this value, the learning algorithm should not give learning oscillations and not provide too slow learning speed in any system to be approximated. Modified momentum is developed and applied to the learning scheme of gradient descent method in order to increase the learning speed. Simulation results assure that this modified momentum provides fast learning speed and also can converge to the optimal point within stable learning process without selecting the momentum coefficient arbitrarily.

In this work, a model for determining the optimal inspection and replacement periods of the safety system in Wolsung nuclear power plant Unit 1 is developed, which is to minimize economic loss caused by inadvertent trip and the system failure. This model uses cost benefit analysis method and the part for optimal inspection period considers the human error. The model is based on three factors as follows : i) The cumulative failure distribution function of the safety system, ii) The probability that the safety system does not operate due to failure of the system or human error when the safety system is needed at an emergency condition and iii) The average probability that the reactor is tripped due to the failure of system components or human error. The model then is applied to evaluate the safety system in Wolsung nuclear power plant Unit 1. The optimal replacement periods which are calculated with proposed model differ from those used in Wolsung NPP Unit 1 by about a few days or months whereas the optimal inspection periods are in about the same range.

In this work a core internal vibration monitoring system which is particularly concerned with the core support barrel (CSB) in ULJIN nuclear power plant unit 1 in Korea is developed. Flow induced vibration and aging processes in the reactor internals cause unsoundness of the internal structure. In particular, the loose-joined flange between the top of the CSB and the head of the vessel may result in core or fuel damage accidents. In order to improve the accuracy of the conventional CSB monitoring system, signals from the piezoelectric accelerometers are used in this work instead of those from the ex-core neutron detectors. This work consists of three parts: one is the development of a suitable tool for detecting the hold down spring broken accident or wearing out of the CSB using the Fuzzy artmap (self-organizing neural network) technique; another is the generation of vibration signals to represent the abnormal states of the CSB by finite element method (FEM) analysis and mock-up experiments; the third is the development of a graphical man-machine interface for the practical use of the monitoring system.

In this paper, an improved algorithm for automatic test pattern generation (ATG) for nuclear power plant digital electronic circuits-the combinational type of logic circuits is presented. For accelerating and improving the ATG process for combinational circuits the presented ATG algorithm has the new concept-the degree of freedom (DF). The DF, directly computed from the system descriptions such as types of gates and their interconnections, is the criterion to decide which among several alternate lines' logic values required along each path promises to be the most effective in order to accelerate and improve the ATG process. Based on the DF the proposed ATG algorithm offers earlier detection of the conflicts and considerable reduction of the number of backtrackings. When the ATG algorithm is implemented in the automatic fault diagnosis system (AFDS) which incorporates the advanced fault diagnosis method of artificial intelligence technique, it is shown that the AFDS using the ATG algorithm makes Universal Card (UV Card) testing much faster than the present testing practice or by using exhaustive testing sets. >

In order to reduce the load of tuning works by trial-and-error for obtaining the best control performance of conventional fuzzy control algorithm, a fuzzy control algorithm with learning function is investigated in this work. This fuzzy control algorithm can make its rule base and tune the membership functions automatically by use of learning function which needs the data from the control actions of the plant operator or other controllers such as PI controller. Learning process in fuzzy control algorithm is to find the optimal values of parameters, which consist of the membership functions and the rule base, by gradient descent method. Learning speed of gradient descent is significantly improved in this work with the addition of modified momentum term. This control algorithm is applied to the steam generator level control by computer simulations. The simulation results confirm the good performance of this control algorithm for level control and show that the fuzzy learning algorithm has the generalization capability for the relation of inputs and outputs and it also has the excellent capability of disturbance rejection.

By using a modified signed directed graph (SDG) together with the distributed artificial neural networks and a knowledge-based system, a method of incipient multi-fault diagnosis is presented for large-scale physical systems with complex pipes and instrumentations such as valves, actuators, sensors, and controllers. The proposed method is designed so as to (1) make a real-time incipient fault diagnosis possible for large-scale systems, (2) perform the fault diagnosis not only in the steady-state case but also in the transient case as well by using a concept of fault propagation time, which is newly adopted in the SDG model, (3) provide with highly reliable diagnosis results and explanation capability of faults diagnosed as in an expert system, and (4) diagnose the pipe damage such as leaking, break, or throttling. This method is applied for diagnosis of a pressurizer in the Kori Nuclear Power Plant (NPP) unit 2 in Korea under a transient condition, and its result is reported to show satisfactory performance of the method for the incipient multi-fault diagnosis of such a large-scale system in a real-time manner. >

In this work, the Fuzzy Signed Digraph(FSD) method which has been researched for the fault diagnosis of industrial process plant systems is improved and applied to the fault diagnosis of the Kori-2 nuclear power plant pressurizer. A method for spurious faults elimination is also suggested and applied to the fault diagnosis. By using these methods, we could diagnose the multi-faults of the pressurizer and could also eliminate the spurious faults of the pressurizer caused by other subsystems. Besides the multi-fault diagnosis and system-wide diagnosis capabilities, the proposed method has many merits such as real-time diagnosis capability, independency of fault pattern, direct use of sensor values, and transparency of the fault propagation to the operators.

Recently, most of the countries using nuclear power for electricity generation are working towards the safer and the more economical designs. In practice some typical nuclear steam supply system (NSSS) vendors are developing evolutionary reactors or passive type reactors based on current technologies. One of the most important features of these advanced reactors is system simplicity. In this study, a model has been presented to quantitatively evaluate system simplification. Design enhancements in simplicity of the chemical and volume control system (CVCS) of the ABB-CE System 80+ and the Korean standardized nuclear power plant (KSNPP) have been evaluated using the presented model. The quantification model is based on the informational entropy concept and system availability analysis. The quantification results indicate that the simplicity of the CVCS is highly dependent on the type and the number of charging pumps. Also, it is found that higher redundancy does not always result in higher availability. Finally, it is stated that the informational entropy concept and the system availability analysis can be a valuable tool for the quantitative evaluation of system simplification.

This paper describes the optimal testing input sets required for fault diagnosis of nuclear power plant digital electronic circuits. With complicated systems such as very large scale integration (VLSI), nuclear power plant (NPP), and aircraft, testing is the major factor of the maintenance of the system. Particularly, the diagnosis time grows quickly with the complexity of the component. For a reduced diagnosis time we derived the optimal testing sets that are the minimal testing sets required for detecting the failure and for locating the failed component. For a reduced diagnosis time, the technique presented by Hayes (1971) fits best for our approach to testing set generation among many conventional methods. However, this method has the following disadvantages: a) it considers only a simple network; b) it concerns only whether the system is in a failed state or not and does not provide the way to locate the failed component. Therefore we have derived the optimal testing input sets that resolve these problems by Hayes while preserving its advantages. When we applied the optimal testing sets to the automatic fault diagnosis system (AFDS) which incorporates the advanced fault diagnosis method of the artificial intelligence technique, we found that fault diagnosis using the optimal testing sets makes testing of the digital electronic circuits much faster than that using exhaustive testing input sets; when we applied them to test the Universal (UV) Card which is a nuclear power plant digital input/output solid state protection system card, we reduced the testing time by up to about 100. >

This paper describes a study on the development of an automatic fault diagnosis system for testing digital electronic circuits of nuclear power plants. Compared with the other conventional fault diagnosis systems, the system described in this paper uses Artificial Intelligence technique of model based reasoning and corroboration, which makes fault diagnosis much more efficient. Since the system diagnoses the circuit boards based only on input and output signals, it can be further developed for on-line testing. The system was implemented on a microprocessor and was applied for Universal Circuit board testing of the Solid State Protection System in nuclear power plants.

Reliability and Risk Issues in Large Scale Safety-critical Digital Control Systems provides some important coverage of reliability issues and their corresponding countermeasures in the field of large-scale digital control systems, from the hardware and software in digital systems to the human operators who supervise the overall process of large-scale systems. Unlike other books which examine theories and issues in individual fields, this book reviews important problems and countermeasures across the fields of hardware and system reliability, software reliability, verification and validation, digital systems, human factors engineering and human reliability analysis. Divided into four sections dealing with hardware and digital system reliability, software reliability, human reliability and human operators in integrated large scale digital systems operation, Reliability and Risk Issues in Large Scale Safety-critical Digital Control Systems offers insights from professional researchers in each specialized field in a diverse yet unified approach.

Reliability issues and some countermeasures for hardware and software of I&C systems in large-scale systems are discussed in Chapters 1–6. Reliability issues and some countermeasures for human operators in large-scale systems are discussed in Chapters 7–9. Reliability issues and countermeasures when the I&C systems and human operators in large-scale systems are considered as a combined entity are in Chapters 10–12. The conventional way of considering I&C systems and human operators as parts of large-scale systems is introduced in Section 10.1. Reliability issues in an integrated model of I&C systems and human operators in large-scale systems are summarized based on some insights from the accidents in large-scale systems in Sections 10.2 and 10.3. Concluding remarks are provided in Section 10.4.

Research and development for enhancing reliability and safety in NPPs have been mainly focused on areas such as automation of facilities, securing safety margin of safety systems, and improvement of main process systems. Studies of TMI-2, Chernobyl, and other NPP events have revealed that deficiencies in human factors, such as poor control room design, procedure, and training, are significant contributing factors to NPPs incidents and accidents [1–5]. Greater attention has been focused on the human factors study. Modern computer techniques have been gradually introduced into the design of advanced control rooms (ACRs) of NPPs as processing and information presentation capabilities of modern computers are increased [6, 7]. The design of instrumentation and control (I&C) systems for various plant systems is also rapidly moving toward fully digital I&C [8, 9]. For example, CRT- (or LCD-) based displays, large display panels (LDP), soft controls, a CPS, and an advanced alarm system were applied to APR-1400 (Advanced Power Reactor-1400) [10]. The role of operators in advanced NPPs shifts from a manual controller to a supervisor or a decision-maker [11] and the operator tasks have become more cognitive works. As a result, HFE became more important in designing an ACR. The human factors engineering program review model (HFE PRM) was developed with the support of U.S. NRC in order to support advanced reactor design certification reviews [4]. The Integrated System Validation (ISV) is part of this review activity.

Reliability and risk issues related to the development of an integrated model of I&C systems and human operators in large-scale systems are discussed in Chapter 10. The development of an integrated model that address these issues is discussed in this chapter. I&C systems and human operators are completely different entities by nature. However, both I&C systems and human operators process information. I&C systems gather information from the plant and processes it to provide automatic control signals to the plant. I&C systems also provide information to human operators in a form that human operators can understand. Human operators receive the information from I&C systems and process it to provide manual control signals for I&C systems. The way information is processed in I&C systems is generally known, while the way information is processed by human operators is not well known. The development of an integrated model of I&C systems and human operators starts from the development of a model for how human operators process incoming information, especially during abnormal or emergency situations.

The concept of software V&V throughout the software development lifecycle has been accepted as a means to assure the quality of safety-critical systems for more than a decade [1]. The Integrated Environment (IE) approach is introduced as one of the countermeasures for V&V (Chapter 5). Adequate tools are accompanied by V&V techniques for the convenience and efficiency of V&V processes. This chapter introduces NuSEE (Nuclear Software Engineering Environment), which is a toolset to support the IE approach developed at Korea Advanced Institute of Science and Technology (KAIST) [2]. The software lifecycle consists of concept, requirements, design, implementation, and test phases. Each phase is clearly defined to separate the activities to be conducted within it. Minimum V&V tasks for safety-critical systems are defined for each phase in IEEE Standard 1012 for “Software Verification and Validation” (Figure 6.1) [3]. V&V tasks are traceable back to the software requirements. A critical software product should be understandable for independent evaluation and testing. The products of all lifecycle phases are also evaluated for software quality attributes, such as correctness, completeness, consistency, and traceability. Therefore, it is critical to define an effective specification method for each software development phase and V&V task based on the effective specifications during the whole software lifecycle.

The possibility of human failure or human error has a significant impact on the safety or reliability of large-scale systems. Most analysis results of accidents, including Chernobyl and TMI-2 accidents indicate that human error is one of the main causes of accidents. Forty-eight percent of incidents in an analysis of 180 significant NPP events occurring in the United States were attributed to failures in human factors [1]. Human factors are analyzed to prevent human errors and are considered in performing a more reliable safety assessment of a system. HRA and human factors are described in Chapter 7 and Chapter 8. An approach to assessing the safety of a system, including human operators, is introduced in Chapter 11, which suggests an integrated safety model that includes both digital control systems and human operators. The adequacy of procedures, stress (available time), training/experience of human operators, and sensor failure probabilities are found to be relatively important compared to other factors in a sensitivity analysis described in Section 11.3.5. The safety of a system is more affected by these four factors. Efficient improvement in the safety of a system is achieved by improving them in the system. Such factors related to humans have been becoming more important than other factors related to hardware and software because only highly reliable hardware and software components can be used in safety-critical systems, such as NPPs.

Electronics is the study of charge flow through various materials and devices, such as semiconductors, resistors, inductors, capacitors, nano-structures, and vacuum tubes [1]. The electronic component is any indivisible electronic building block packaged in a discrete form with two or more connected leads or metallic pads. The components are intended to be connected together, usually by soldering to a printed circuit board, to create an electronic circuit with a particular function. The representative electronic components are integrated circuits (microprocessors, RAM), resistors, capacitors, and diodes. These electronic components are the major hardware components making up digital systems.

An approach to improve human reliability is introduced in this chapter. Major methods to analyze human reliability have been reviewed in Chapter 7. Chapter 8 presents human factors-related activities to design a human-machine interface (HMI), especially for nuclear power plant (NPP) applications. Human factors engineering (HFE) is strictly applied in the nuclear industry. Designing a good HMI enhances human reliability and prevents human errors, as well as helping with training and proceduralization. An HFE process to design an HMI for a safety critical system that requires high reliability for operators consists of three steps: analysis, design, and verification & validation (V&V).

For the advanced main control rooms (MCRs), various types of decision support systems have been developed. It is very important to design highly reliable decision support systems in order to adapt them in actual MCRs. In addition, to evaluate those support systems and validate their efficiency and reliability is as important as to design highly reliable decision support systems, because inappropriate decision support systems or automation systems can cause adverse effects. In this work, decision support systems are evaluated experimentally. The target system of this work is the integrated decision support system including an alarm system, a fault diagnosis system, a computerized procedure system, and an operation validation system. The result of the experiment showed that decision support functions reduce subjects' workload and failure probabilities.

In safety critical systems, especially in nuclear power plants (NPPs), human error has been introduced as one of the serious causes of accidents. In order to prevent human errors, many efforts have been made to improve main control room (MCR) interface designs and to develop decision support systems that allow convenient MCR operation and maintenance. In this paper, an integrated decision support system to aid the cognitive process of operators is proposed for advanced MCRs in future NPPs. This work suggests support system design considered an operator's cognitive process. Various kinds of support systems for advanced MCRs have been developed or are in development. Therefore, a design basis regarding what kinds of support systems are appropriate for MCR operators is necessary. The proposed system supports not merely a particular task, but also the entire operation process based on a human cognitive process model. It supports the operator's entire cognitive process by integrating support systems that support each cognitive activity. Furthermore, two decision support systems are developed. The fault diagnosis advisory system is to make the task of fault diagnosis easier and to reduce errors by quickly suggesting likely faults based on the highest probability of their occurrence. The operation validation system is to provide an advisory function to supervise and validate the operator's actions during abnormal environments

As programmable logic controllers (PLCs) are widely used in the digital instrumentation and control (I&C) systems of nuclear power plants (NPPs), the safety of PLC software has become the most important consideration. In this work, we propose a method to perform effective verification activities on the traceability analysis and the software design evaluation in the software design phase. In order to perform the traceability analysis between software requirement specification (SRS) written in a natural language and software design specification (SDS) written in function block diagram (FBD), this method uses extended- structured decision table (ESDT). ESDTs include information related to the traceability analysis from SRS and SDS, respectively. Through comparing with two ESDTs, an effective traceability analysis can be achieved. For the software design evaluation, we use model checking as a formal verification method. FBD-style design specification is translated into symbolic model verifier (SMV) input language and then the FBD-style design specification can be formally analyzed using SMV model checker.

A risk-informed safety significance categorization (RISSC) is to categorize structures, systems, or components (SSCs) of a nuclear power plant (NPP) into two or more groups, according to their safety significance using both probabilistic and deterministic insights [1]. In the conventional methods for the RISSC, SSCs are quantitatively categorized according to their importance measures for the initial categorization [2]. The final categorizations of SSCs, however, are qualitatively made by expert panel through discussions and adjustments of opinions by using the probabilistic insights compiled in the initial categorization process and combining the probabilistic insights with the deterministic insights. Therefore, owing to the qualitative decision-making process, the conventional methods have the demerits that they are very costly in terms of time and labour; and that it is not easy to reach the final decision, when the opinions of the experts are in conflict.

A backward safety analysis method that can deal with both severity and risk by virtue of fuzzy theory is proposed in this work. Fuzzy Colored Petri Net (FCPN) is developed and applied to the safety analysis method. The feasibility of this method is demonstrated through a popular example.

The objective of the paper is to present a safety evaluation method. Fuzzy colored Petri nets (FCPN), which are a fuzzy Petri net model based on colored Petri nets, are introduced as the basis of the evaluation. FCPN prove to be useful to evaluate software safety through the safety evaluation method proposed in this work.