Attack–norm separation for detecting attack‐induced quality problems on computers and networks

Cyber attacks on computer and network systems induce system quality and reliability problems, and present a significant threat to the computer and network systems that we are heavily dependent on. Cyber attack detection involves monitoring system data and detecting the attack‐induced quality and reliability problems of computer and network systems caused by cyber attacks. Usually there are ongoing normal user activities on computer and network systems when an attack occurs. As a result, the observed system data may be a mixture of attack data and normal use data (norm data). We have established a novel attack–norm separation approach to cyber attack detection that includes norm data cancelation to improve the data quality as an important part of this approach. Aiming at demonstrating the importance of norm data cancelation, this paper presents a set of data modeling and analysis techniques developed to perform norm data cancelation before applying an existing technique of anomaly detection, the chi‐square distance monitoring (CSDM), to residual data obtained after norm data cancelation for cyber attack detection. Specifically, a Markov chain model of norm data and an artificial neural network (ANN) of norm data cancelation are developed and tested. This set of techniques is compared with using CSDM alone for cyber attack detection. The results show a significant improvement of detection performance by CSDM with norm data cancelation over CSDM alone. Copyright © 2006 John Wiley & Sons, Ltd.     

核电领域电器安全保护设备校准方法

叙述核电站日常使用的电器安全保护设备——微机继电保护测试系统、串联谐振测试系匆和高压开关机械特性测试系统的校准方案和方法。着重讨论这些校准方法中的技术难点,并对校准结果的不确定度进行了分析。这将有效地提高核电领域电器安全保护设备的可靠性和准确性,从而面保核电的运营安全。     

Electronics in nuclear power programme of India—An overview

This paper presents an overview of state-of-the art developments in electronics for nuclear power programme of India. Indigenous activities in instrumentation and control (I&C) in the areas of detector development, nuclear instrumentation, monitoring and control electronics and special sensors paved the way to self-reliance in nuclear industry. Notable among the recent I&C systems developed for 540 MWe reactors are Liquid Zone Control System (LZCS), flux mapping system and advance reactor regulating system. In a nuclear plant, apart from ensuring functional requirements, design of electronics needs to meet high level of reliability, safety and security standards. Therefore, a lot of importance is attached to activities such as design review, testing, operation, maintenance and qualifications of I&C systems. Induction of computer based I&C systems mandated a rigorous verification process commensurate with the safety class of the system as specified in Atomic Energy Regulatory Board (AERB) safety guides. Software reliability is assured by following strict development life cycle combined with zero-defect policy and is verified through verification and validation (V&V) process. Development of new techniques in data transmissions with optical fibres as transmission medium and wireless networks in control systems is being pursued. With new I&C systems, efforts were made to utilize the same hardware and software platforms for various plant applications, i.e., for standardization. Thrust was given to use Field Programmable Gate Arrays (FPGA) and Application Specific Integrated Circuits (ASIC) in order to improve the reliability of system by reducing component count. It has become imperative to develop modern contemporary solutions like ASICs, HMCs, System on Chip (SOC) and detector mounted electronics and towards that various ASICs and HMCs have been developed in-house to meet the challenges.     

An operator support system for research reactor operations and fault diagnosis through a connectionist framework and PSA based knowledge based systems

During reactor upset/abnormal conditions, emphasis is placed on the plant operator's ability to quickly identify the problem and perform diagnosis and initiate recovery action to ensure the safety of the plant. However, the reliability of human action is adversely affected at the time of crisis due to time stress and psychological factors. The availability of operational aids capable of monitoring the status of the plant and quickly identifying the deviation from normal operation is expected to significantly improve the operator reliability.The development of operator support systems using probabilistic safety assessment (PSA) techniques and information is finding wide application in nuclear plant operation. Often it is observed that most of the applications use a rule-based approach for diagnosis as well as safety status/transient conditions monitoring. A more efficient approach using artificial neural networks for safety status/transient condition monitoring and rule-based systems for diagnosis and emergency procedure generation has been applied for the development of a prototype operator adviser (OPAD) system for a 100 MW(th) heavy water moderated, cooled and natural uranium fueled research reactor. The development objective of this system is to improve the reliability of operator action and hence the reactor safety at the time of crisis as well as in normal operation. In order to address safety objectives at various stages of development of OPAD, the PSA techniques and tools have been used for knowledge representation. It has been demonstrated, with recall tests on the artificial neural network, that it can efficiently identify the reactor status in real-time scenario. This paper discusses various issues related to the development of an operator support system in a comprehensive way, right from the study of safety objectives, to data collection, to implementation of such a system.     

Application of artificial neural networks to nuclear power plant transient diagnosis

A study on various artificial neural network (ANN) algorithms for selecting a best suitable algorithm for diagnosing the transients of a typical nuclear power plant (NPP) is presented. NPP experiences a number of transients during its operations. These transients may be due to equipment failure, malfunctioning of process systems, etc. In case of any undesired plant condition generally known as initiating event (IE), the operator has to carry out diagnostic and corrective actions. The objective of this study is to develop a neural network based framework that will assist the operator to identify such initiating events quickly and to take corrective actions. Optimization study on several neural network algorithms has been carried out. These algorithms have been trained and tested for several initiating events of a typical nuclear power plant. The study shows that the resilient-back propagation algorithm is best suitable for this application. This algorithm has been adopted in the development of operator support system. The performance of ANN for several IEs is also presented.     

A MODEL FOR PREDICTING THE RELIABILITY OF A MAN–MACHINE SYSTEM

A continuous-time Markov chain model, which considers the concept of human errors and recovery factors, is proposed for analysing the reliability of a man–machine system. This model can be applied to industrial systems such as the nuclear power plant, the NC machine cell, and the chemical plant. In order to show the application of the model and to validate its result, a computer animation program was written to simulate the control operation of a system. Some simulation experiments were conducted and the data were fed into the reliability model. The results show that the proposed model can perform well in comparison to the simulation output. In addition, some observations from the simulation experiments can also be applied to the design of a control system. © 1997 John Wiley & Sons, Ltd.     

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

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 operator's cognitive process showed better performance compared to independent decision support systems.     

A method for evaluating fault coverage using simulated fault injection for digitalized systems in nuclear power plants

The fault coverage for digital system in nuclear power plants is evaluated using a simulated fault injection method. Digital systems have numerous advantages, such as hardware elements share and hardware replication of the needed number of independent channels. However, the application of digital systems to safety-critical systems in nuclear power plants has been limited due to reliability concerns. In the reliability issues, fault coverage is one of the most important factors. In this study, we propose an evaluation method of the fault coverage for safety-critical digital systems in nuclear power plants. The system under assessment is a local coincidence logic processor for a digital plant protection system at Ulchin nuclear power plant units 5 and 6. The assessed system is simplified and then a simulated fault injection method is applied to evaluate the fault coverage of two fault detection mechanisms. From the simulated fault injection experiment, the fault detection coverage of the watchdog timer is 44.2% and that of the read only memory (ROM) checksum is 50.5%. Our experiments show that the fault coverage of a safety-critical digital system is effectively quantified using the simulated fault injection method.     

Determination of design alternatives and performance criteria for safety systems in a nuclear power plant via simulated annealing

This study presents an efficient methodology that derives design alternatives and performance criteria for safety functions/systems in commercial nuclear power plants. Determination of the design alternatives and intermediate-level performance criteria is posed as a reliability allocation problem. The reliability allocation is performed in a single step by means of the concept of two-tier noninferior solutions in the objective and risk spaces within the top-level probabilistic safety criteria (PSC). Two kinds of two-tier noninferior solutions are obtained: desirable design alternatives and intolerable intermediate-level PSC of safety functions/systems.The weighted Chebyshev norm (WCN) approach with an improved Metropolis algorithm in simulated annealing is used to find the two-tier noninferior solutions. This is very efficient in searching for the global minimum of the difficult multiobjective optimization problem (MOP) which results from strong nonlinearity of a probabilistic safety assessment (PSA) model and nonconvexity of the problem. The methodology developed in this study can be used as an efficient design tool for desirable safety function/system alternatives and for the determination of intermediate-level performance criteria.The methodology is applied to a realistic streamlined PSA model that is developed based on the PSA results of the Surry Unit 1 nuclear power plant. The methodology developed in this study is very efficient in providing the intolerable intermediate-level PSC and desirable design alternatives of safety functions/systems.     

A computational model for evaluating the effects of attention, memory, and mental models on situation assessment of nuclear power plant operators

Operators in nuclear power plants have to acquire information from human system interfaces (HSIs) and the environment in order to create, update, and confirm their understanding of a plant state, as failures of situation assessment may cause wrong decisions for process control and finally errors of commission in nuclear power plants. A few computational models that can be used to predict and quantify the situation awareness of operators have been suggested. However, these models do not sufficiently consider human characteristics for nuclear power plant operators.In this paper, we propose a computational model for situation assessment of nuclear power plant operators using a Bayesian network. This model incorporates human factors significantly affecting operators’ situation assessment, such as attention, working memory decay, and mental model.As this proposed model provides quantitative results of situation assessment and diagnostic performance, we expect that this model can be used in the design and evaluation of human system interfaces as well as the prediction of situation awareness errors in the human reliability analysis.     

Comparison of Markov model and fault tree approach in determining initiating event frequency for systems with two train configurations

This study explores the use of Markov models in some areas of systems analysis in which time evolution of the system may be a significant factor in influencing the system reliability or availability. Comparisons are made between the Markov models and the time-averaged fault tree models for determining support system failure initiating event frequency in a nuclear power plant, for both power and shutdown conditions. Factors affecting consistency between the fault tree approach and the Markov model approach are studied for systems with common two train configurations. A correlation is developed to estimate the ratio between initiator frequencies through both approaches for a two parallel component system. Insights are developed as to when time averaged and simplified fault tree models support a good approximation to the more rigorous time-dependent Markov models.     

基于15F2K60S2的农用无人机电源监控仪表设计

为解决农用无人机电源监控系统稳定性差、成本高、通信效果不好和功能单一等问题，设计了一种以嵌入式微处理器为核心的农用无人机电源监控仪表。该仪表以15F2K60S2嵌入式微处理器为平台，集成CS5460数据采集模块和ADM2483通信模块，在单片机C语言和汇编语言指令的基础上，结合仪表总体结构，设计开发了农用无人机电源监控仪表的硬件电路和软件程序。采用多功能校准仪、信号发生器和示波器等测试工具对所设计的电源监控仪表进行稳定性和可靠性实验，并分析了可能引起测量误差的原因。结果表明：该仪表具有良好的稳定性和可靠性，在0~500 V的输入电压范围内，其测量精度达到了工业标准2.0级要求，完全能满足农用无人机电源监控系统的需求。研究结果能为农用无人机电源监控系统后续更深入的研究提供一定的理论参考，给相关企业开发农用无人机电源监控系统提供有效指导。     

Application of Hybrid Learning Neural Fuzzy Systems in Reliability Prediction

This study presents a hybrid learning neural fuzzy system for accurately predicting system reliability. Neural fuzzy system learning with and without supervision has been successfully applied in control systems and pattern recognition problems. This investigation modifies the hybrid learning fuzzy systems to accept time series data and therefore examines the feasibility of reliability prediction. Two neural network systems are developed for solving different reliability prediction problems. Additionally, a scaled conjugate gradient learning method is applied to accelerate the training in the supervised learning phase. Several existing approaches, including feed‐forward multilayer perceptron (MLP) networks, radial basis function (RBF) neural networks and Box–Jenkins autoregressive integrated moving average (ARIMA) models, are used to compare the performance of the reliability prediction. The numerical results demonstrate that the neural fuzzy systems have higher prediction accuracy than the other methods. Copyright © 2005 John Wiley & Sons, Ltd.     

Development of an advanced human–machine interface for next generation nuclear power plants

An advanced human–machine interface (HMI) has been developed to enhance the safety and availability of a nuclear power plant (NPP) by improving operational reliability. The key elements of the proposed HMI are the large display panels which present synopsis of plant status and the compact, computer-based work stations for monitoring, control and protection functions. The work station consists of four consoles such as a dynamic alarm console (DAC), a system information console (SIC), a computerized operating-procedure console (COC), and a safety system information console (SSIC). The DAC provides clean alarm pictures, in which information overlapping is excluded and alarm impacts are discriminated, for quick situation awareness. The SIC supports a normal operation by offering all necessary system information and control functions over non-safety systems. In addition, it is closely linked to the other consoles in order to automatically display related system information according to situations of the DAC and the COC. The COC aids operators with proper operating procedures during normal plant startup and shutdown or after a plant trip, and it also reduces their physical/mental burden through soft automation. The SSIC continuously displays safety system status and enables operators to control safety systems. The proposed HMI has been evaluated using the checklists that are extracted from various human factors guidelines. From the evaluation results, it can be concluded that the HMI is so designed as to address the human factors issues reasonably. After sufficient validation, the concept and the design features of the proposed HMI will be reflected in the design of the main control room of the Korean Next Generation Reactor (KNGR).     

Neural model for forecasting temperature in a distribution network of cooling water supplied to systems producing petroleum products

This article presents the application of a neural model of heat transfer for the purpose of forecasting temperature at selected points of a circulating water ring network. The purpose of a circulating water system is to lower the temperature of petroleum products manufactured on numerous petrochemical lines at a Polish petrochemical plant. Temperature forecasting at 96 nodes of the circulating water system, significant from the point of view of system operation, is carried out using SVM neural networks. Neural networks learn based on archival data recorded in the process parameter monitoring system. Thermal, hydraulic and control parameters of the cooling process, as well as weather variables, constitute crucial input data for the neural model. The temperature forecasting algorithm has been implemented in a computer program that was then applied and remains in use for temperature forecasting in a maintenance department of an industrial plant.     

From LESSEPS to the workstation for reliability engineers

Three Mile Island and Chernobyl in the nuclear industry, Challenger, in the space industry, Seveso and Bhopal in the chemical industry—all these accidents show how difficult it is to forecast all likely accident scenarios that may occur in complex systems. This was, however, the objective of the probabilistic safety assessment (PSA) performed by EDF at the Paluel nuclear power plant. The full computerization of this study led to the LESSEPS project, aimed at automating three different steps: generation of reliability models—based on the use of expert systems, qualitative and quantitative processing of these models using computer codes, and overall management of PSA studies. This paper presents the results obtained and the gradual transformation of this first generation of tools into a workstation aimed at integrating reliability studies at all stages of an industrial process.     

Fault Diagnosis of Power Electronic Circuits Based on Adaptive Simulated Annealing Particle Swarm Optimization

In the field of energy conversion, the increasing attention on power electronic equipment is fault detection and diagnosis. A power electronic circuit is an essential part of a power electronic system. The state of its internal components affects the performance of the system. The stability and reliability of an energy system can be improved by studying the fault diagnosis of power electronic circuits. Therefore, an algorithm based on adaptive simulated annealing particle swarm optimization (ASAPSO) was used in the present study to optimize a backpropagation (BP) neural network employed for the online fault diagnosis of a power electronic circuit. We built a circuit simulation model in MATLAB to obtain its DC output voltage. Using Fourier analysis, we extracted fault features. These were normalized as training samples and input to an unoptimized BP neural network and BP neural networks optimized by particle swarm optimization (PSO) and the ASAPSO algorithm. The accuracy of fault diagnosis was compared for the three networks. The simulation results demonstrate that a BP neural network optimized with the ASAPSO algorithm has higher fault diagnosis accuracy, better reliability, and adaptability and can more effectively diagnose and locate faults in power electronic circuits.     

Symptom based diagnostic system for nuclear power plant operations using artificial neural networks

Nuclear power plant experiences a number of transients during its operations. These transients may be due to equipment failure, malfunctioning of process systems and unavailability of safety systems. In such a situation, the plant may result into an abnormal state which is undesired. In case of an undesired plant condition generally known as an initiating event (IE), the operator has to carry out diagnostic and corrective actions. The operator's response may be too late to mitigate or minimize the negative consequences in such scenarios. The objective of this work is to develop an operator support system based on artificial neural networks that will assist the operator to identify the IEs at the earliest stages of their developments. These abnormal plant conditions must be diagnosed and identified through the process instrument readings. A symptom based diagnostic system has been developed to investigate the IEs. The event identification is carried out by using resilient back propagation neural network algorithm. Whenever an event is detected, the system will display the necessary operator actions in addition to the type of IE. The system will also show the graphical trend of relevant parameters. The developed system is able to identify the eight IEs of Narora Atomic Power Station. This paper describes the features of the diagnostic system taking one of the IEs as a case study.     

高可靠智能压力开关研发及应用

压力开关是火电机组保护系统中重要的一次信号元件,压力开关测量的准确性、可靠性及快速性直接决定了电厂保护系统能否正确、快速动作。针对机械式压力开关和压力变送器作为保护元件存在的不足,依据保护、调节、监视三分开控制系统设计原则,设计了微功耗、高可靠的两线制智能数显压力开关,并对智能压力开关进行可靠性设计和试验测试。测试结果表明,该压力开关有效地解决了传统压力开关存在的卡涩、偏移、不可监控和预测的问题,具有微功耗、高可靠、高精度和免校验等优点,为电厂保护系统的可靠运行提供了根本有效的保证。     

Expanding the scope of DYLAM methodology to study the dynamic reliability of complex systems: the case of chemical and volume control in nuclear power plants

The aim of this paper is to show how the DYLAM methodology, normally used to study the reliability of relatively simple plants, can be extended to the dynamic analysis of complex systems. To realize this goal a first step of classical reliability analysis on the plant under study is necessary, in order to discover its most critical parts. Subsequently a dynamic reliability analysis by DYLAM may be performed, focusing on those parts identified as critical elements. We show an application of these concepts to the study of the chemical and volume control system of a nuclear power plant, whereby the reduction of the components to be studied by DYLAM has been obtained by a detailed qualitative engineering analysis of the plant layout. Some results are presented to demonstrate the potential of the methodology to model the interaction of physical and probabilistic analysis of the plant.