Poong Hyun Seong

A probabilistic approach for determining the control mode in CREAM

The control mode is the core concept for the prediction of human performance in CREAM. In this paper, we propose a probabilistic method for determining the control mode which is a substitute for the existing deterministic method. The new method is based on a probabilistic model, a Bayesian network. This paper describes the mathematical procedure for developing the Bayesian network for determining the control mode. The Bayesian network developed in this paper is an extension of the existing deterministic method. Using the Bayesian network, we expect that we can get the best estimate of the control mode given the available data and information about the context. The mathematical background and procedure for developing equivalent Bayesian networks for given discrete functions provided in this paper can be applied to other discrete functions to develop probabilistic models.

An analytic model for situation assessment of nuclear power plant operators based on Bayesian inference

Simulation-based human reliability analysis (HRA) methods such as IDAC seem to provide a new direction for the development of advanced HRA methods. In such simulation-based HRA methods, the simulation model for the situation assessment of nuclear power plant (NPP) operators is essential, especially for addressing the issue of errors-of-commission (EOCs). Therefore, we propose an analytic model for the situation assessment of NPP operators based on Bayesian inference. The proposed model is found to be able to address several important features of the situation assessment of NPP operators, and is expected to provide good approximations to some parts of the situation assessment. A comparison with an existing model and identification of several other features of the situation assessment of NPP operators that should be further addressed are also provided.

Development of Human Performance Measures for Human Factors Validation in the Advanced MCR of APR-1400

Main control room (MCR) man-machine interface (MMI) design of advanced nuclear power plants (NPPs) such as APR (advanced power reactor)-1400 can be validated through performance-based tests to determine whether it acceptably supports safe operation of the plant. In this paper, plant performance, personnel task performance, situation awareness, workload, teamwork, and anthropometric/physiological factor are considered for the human performance evaluation. For the development of human performance measures, attention is paid to considerations and constraints such as the changed environment in an advanced MCR, needs for a practical and economic evaluation, and suitability of evaluation criteria. Measures generally used in various industries and empirically proven to be useful are adopted as the main measures with some modifications. In addition, complementary measures are developed to overcome some of the limitations associated with the main measures. The development of the measures is addressed based on theoretical and empirical background. Finally we discuss the way in which the measures can be effectively integrated. The HUPESS (HUman Performance Evaluation Support System) which is in development is also briefly introduced.

A computational method for probabilistic safety assessment of I&C systems and human operators in nuclear power plants

To make probabilistic safety assessment (PSA) more realistic, the improvements of human reliability analysis (HRA) are essential. But, current HRA methods have many limitations including the lack of considerations on the interdependency between instrumentation and control (I&C) systems and human operators, and lack of theoretical basis for situation assessment of human operators. To overcome these limitations, we propose a new method for the quantitative safety assessment of I&C systems and human operators. The proposed method is developed based on the computational models for the knowledge-driven monitoring and the situation assessment of human operators, with the consideration of the interdependency between I&C systems and human operators. The application of the proposed method to an example situation demonstrates that the quantitative description by the proposed method for a probable scenario well matches with the qualitative description of the scenario. It is also demonstrated that the proposed method can probabilistically consider all possible scenarios and the proposed method can be used to quantitatively evaluate the effects of various context factor on the safety of nuclear power plants. In our opinion, the proposed method can be used as the basis for the development of advanced HRA methods.

DEVELOPMENT OF AN INTEGRATED DECISION SUPPORT SYSTEM TO AID COGNITIVE ACTIVITIES OF OPERATORS

As digital and computer technologies have grown, human-machine interfaces (HMIs) have evolved. In safety-critical systems, especially in nuclear power plants (NPPs), HMIs are important for reducing operational costs, the number of necessary operators, and the probability of accident occurrence. Efforts have been made to improve main control room (MCR) interface design and to develop automated or decision support systems to ensure convenient operation and maintenance. In this paper, an integrated decision support system to aid operator cognitive processes is proposed for advanced MCRs of future NPPs. This work suggests the design concept of a decision support system which accounts for an operators cognitive processes. The proposed system supports not only a particular task, but also the entire operation process based on a human cognitive process model. In this paper, the operators operation processes are analyzed according to a human cognitive process model and appropriate support systems that support each cognitive process activity are suggested.

An analytical approach to quantitative effect estimation of operation advisory system based on human cognitive process using the Bayesian belief network

Abstract The design of instrumentation and control (I&C) systems for nuclear power plants (NPPs) is rapidly moving towards fully digital I&C systems and is trending towards the introduction of modern computer techniques into the design of advanced main control rooms (MCRs) of NPPs. In the design of advanced MCRs, human–machine interfaces have improved and various types of decision support systems have been developed. It is important to design highly reliable decision support systems in order to adapt them in actual NPPs. In addition, to evaluate decision support systems in order to validate their efficiency is as important as to design highly reliable decision support systems. In this paper, an operation advisory system based on the human cognitive process is evaluated in order to estimate its effect. The Bayesian belief network model is used in the evaluation of the target system, and a model is constructed based on human reliability analysis event trees. In the evaluation results, a target system based on the operators cognitive process showed better performance compared to independent decision support systems.

Incipient multiple fault diagnosis in real time with application to large-scale systems

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. >

Experiments on orientation recovery and steering of an autonomous mobile robot using encoded magnetic compass disc

This paper describes the experiments on orientation recovery and steering of an autonomous mobile robot which uses an encoded magnetic compass disc as an orientation sensor. The mobile robot developed for experiments in this work is highly cost-effective and has two independent position-sensing systems. The one is an odometric sensor installed on the passive castor wheel, and the other consists of an optical incremental shaft encoder using magnetic compass disc. The experiments have been performed on two cases: (1) line path tracking test in a slippery environment and (2) orientation steering test in a circular path. Experimental results show that, while line path tracking in a slippery environment, the orientation of the mobile robot is recovered successfully with an acceptable position shift after two-time recovery action. For the circular path test case, the strayed angle after 90/spl deg/ orientation steering is only about 0.30/spl deg/. It is shown that the system designed is simple and applicable to mobile robot navigation.

Reliability graph with general gates: an intuitive and practical method for system reliability analysis

Abstract 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.

The effect of information types on diagnostic strategies in the information aid

Abstract Through experiments, this paper investigates the compatibility of information types with the diagnostic strategy in information aid. Compatibility with operator strategies is an important requirement for information aiding systems in nuclear power plants (NPPs). This paper used three typical types of information aids for MCR operators to investigate the effect of the aids on diagnostic strategies: “alarm (A),” “hypothesis on faults (H),” and “hypothesis on faults and expected symptoms (HS).” The experimental results indicate that the effectiveness of information aid types can vary, dependent on the strategies subjects employ. The results also show that the HS aid improved the diagnosis performance in the hypothesis-and-test strategy.