• Journal of Advanced Marine Engineering and Technology
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• 제36권5호
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• pp.662-673
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• 2012

This study introduces a condition diagnosis technique for a turbine governor system. The governor system is an important control system to handle turbine speed in a nuclear power plant. The turbine governor system includes turbine valves and stop valves which have their own functions in the system. Because a turbine governor system is operated by high oil pressure, it is very difficult to maintain under stable operating conditions. Turbine valves supply oil pressure to the governor system for proper operation. Using the pressure variation of turbine and governor valves, operating conditions of the turbine governor control system are detected and identified. To achieve automatic detection of valve status, time-based and frequency-based analysis is employed. In this study, a new approach, wavelet decomposition, was used to extract specific features from the pressure signals of the governor and stop valves. The extracted features, which represent the operating conditions of the turbine governor system, include important information to control and diagnose the valves. After extracting the specific features, decision rules were used to classify the valve conditions. The rules were generated by a decision tree algorithm (a typical simple method for data-based rule generation). The results given by the wavelet-based analysis were compared to detection results using time- and frequency-based approaches. Compared with the several related studies, the wavelet transform-based analysis, the proposed in this study has the advantage of easier application without auxiliary features.

• 대한기계학회논문집
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• 제18권2호
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• pp.323-338
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• 1994

This paper outlines a framework for performing intelligent sensor validation for a diagnostic expert system while reasoning under uncertainty. The emphasis is on the algorithmic preprocess technique. A companion paper focusses on heuristic post-processing. Sensor validation plays a vital role in the ability of the overall system to correctly detemine the state of a plant monitored by imperfect sensors. Especially, several theoretical developments were made in understanding uncertain sensory data in statistical aspect. Uncertain information in sensory values is represented through probability assignments on three discrete states, "high", "normal", and "low", and additional sensor confidence measures in Algorithmic Sv.Upper and lower warning limits are generated from the historical learning sets, which represents the borderlines for heat rate degradation generated in the Algorithmic SV initiates a historic data base for better reference in future use. All the information generated in the Algorithmic SV initiate a session to differentiate the sensor fault from the process fault and to make an inference on the system performance. This framework for a diagnostic expert system with sensor validation and reasonig under uncertainty applies in HEATXPRT$^{TM}$, a data-driven on-line expert system for diagnosing heat rate degradation problems in fossil power plants.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.953-958
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• 1993

We develop a neural network control algorithm for the ACS (Advanced Control System). The ACS is an extended version of the DCS (Distributed Control System) to which functions of fault detection and diagnosis and advanced control algorithms are added such as neural networks, fuzzy logics, and so on. In spite of its usefulness proven by computer simulations, the neural network control algorithm, as far as we know, has no tool which makes it applicable to process control. It is necessary that the neural network controller should be turned into the function code for its application to the ACS. So we develop a general method to implement the neural network control systems for the ACS. By simulations using the simulator for the boiler of 'Seoul fire power plant unit 4', the methodology proposed in this paper is validated to have the applicability to process control.

• 한국시뮬레이션학회논문지
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• 제29권3호
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• pp.9-18
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• 2020

대규모 화력 발전소 제어용 시뮬레이터 개발은 급수/증기 계통, 공기/연소가스 계통, 미분탄 계통 및 터빈/발전기 계통으로 구성되며, 기계적인 터빈/발전기를 제외하고 모든 계통에 대하여 모델링이 가능하다. 현재까지 화력발전의 일부 계통에 대한 신경망 시뮬레이터 개발에 대한 시도는 있었으나 전체 계통에 대한 시뮬레이터 개발은 완성된 적이 없다. 특히 모든 발전사의 핵심 기술 개발중 하나인 오토튜닝은 정확도가 높은 모든 계통에 대한 모델링이 완성되어야 이룰 수 있는 기술이다. 이에 본 논문은 신경망 알고리즘을 이용하여 시스템을 설계할 경우 가장 핵심인 입출력 관계에 대한 변수를 모든 계통에 대하여 정의하였다. 시뮬레이션을 수행한 결과 실제 보일러 계통의 95~99% 이상 정확도를 보임에 따라 본 시뮬레이터에 현장 PID 제어기를 결합할 경우 고장진단이나 오토튜닝에 활용 가능할 것이다.

• Nuclear Engineering and Technology
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• 제52권12호
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• pp.2687-2698
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• 2020

Most of the machine learning-based intrusion detection tools developed for Industrial Control Systems (ICS) are trained on network packet captures, and they rely on monitoring network layer traffic alone for intrusion detection. This approach produces weak intrusion detection systems, as ICS cyber-attacks have a real and significant impact on the process variables. A limited number of researchers consider integrating process measurements. However, in complex systems, process variable changes could result from different combinations of abnormal occurrences. This paper examines recent advances in intrusion detection algorithms, their limitations, challenges and the status of their application in critical infrastructures. We also introduce the discussion on the similarities and conflicts observed in the development of machine learning tools and techniques for fault diagnosis and cybersecurity in the protection of complex systems and the need to establish a clear difference between them. As a case study, we discuss special characteristics in nuclear power control systems and the factors that constraint the direct integration of security algorithms. Moreover, we discuss data reliability issues and present references and direct URL to recent open-source data repositories to aid researchers in developing data-driven ICS intrusion detection systems.

• 한국측량학회지
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• 제34권6호
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• pp.619-627
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• 2016

Cells of a PV (photovoltaic) module can suffer defects due to various causes resulting in a loss of power output. As a malfunctioning cell has a higher temperature than adjacent normal cells, it can be easily detected with a thermal infrared sensor. A conventional method of PV cell inspection is to use a hand-held infrared sensor for visual inspection. The main disadvantages of this method, when applied to a large-scale PV power plant, are that it is time-consuming and costly. This paper presents an algorithm for automatically detecting defective PV panels using images captured with a thermal imaging camera from an UAV (unmanned aerial vehicle). The proposed algorithm uses statistical analysis of thermal intensity (surface temperature) characteristics of each PV module to verify the mean intensity and standard deviation of each panel as parameters for fault diagnosis. One of the characteristics of thermal infrared imaging is that the larger the distance between sensor and target, the lower the measured temperature of the object. Consequently, a global detection rule using the mean intensity of all panels in the fault detection algorithm is not applicable. Therefore, a local detection rule was applied to automatically detect defective panels using the mean intensity and standard deviation range of each panel by array. The performance of the proposed algorithm was tested on three sample images; this verified a detection accuracy of defective panels of 97% or higher. In addition, as the proposed algorithm can adjust the range of threshold values for judging malfunction at the array level, the local detection rule is considered better suited for highly sensitive fault detection compared to a global detection rule. In this study, we used a panel area extraction method that we previously developed; fault detection accuracy would be improved if panel area extraction from images was more precise. Furthermore, the proposed algorithm contributes to the development of a maintenance and repair system for large-scale PV power plants, in combination with a geo-referencing algorithm for accurate determination of panel locations using sensor-based orientation parameters and photogrammetry from ground control points.

• 전기학회논문지
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• 제64권12호
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• pp.1768-1775
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• 2015

Recent major domestic facilities, such as nuclear power plants, many control cables are installed and are degraded by long-term use, but research on deterioration diagnosis is lacking. In the event of a fault in the cable due to deterioration can be developed into a major accident such as the main plant is stopped, so the deterioration diagnostic techniques of high reliability for the cable is required. In this paper, proposes a methodology using a cable resonance that can effectively diagnose the deterioration of the cable. Prior to the test, we developed a setup for stable measuring the characteristics of the cable and it verified the suitable of the measurement set-up in terms of interactivity and reliability, also measured S-parameters applying verified measurement set-up to the cables that deterioration degree is different. Then, we had amplified the difference in resonance frequency between the healthy state and the deteriorated state using connection in a series of measured S-parameters. In a result from the method, we have verified that the more deteriorate the cables is, the more decrease the resonance frequency is. Measured results are justified by inducing the resonance frequency calculation of the cable from the S- parameters represented by the hyperbolic function formula. VNA(Vector Network Analyzer) for S-parameter measurements used in this study is Agilent E5061B and shielded twisted-pair cables was used for deterioration diagnostic test.

• 한국IT서비스학회지
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• 제20권3호
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• pp.1-12
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• 2021

Reactor Coolant Pump (RCP) is core part of nuclear power plant to provide the forced circulation of reactor coolant for the removal of core heat. Properly monitoring vibration of RCP is a key activity of a successful predictive maintenance and can lead to a decrease in failure, optimization of machine performance, and a reduction of repair and maintenance costs. Here, we developed real-time RCP Vibration Analysis System (VAS) that web based platform using NoSQL DB (Mongo DB) to handle vibration data of RCP. In this paper, we explain how to implement digital signal process of vibration data from time domain to frequency domain using Fast Fourier transform and how to design NoSQL DB structure, how to implement web service using Java spring framework, JavaScript, High-Chart. We have implement various plot according to standard of the American Society of Mechanical Engineers (ASME) and it can show on web browser based on HTML 5. This data analysis platform shows a upgraded method to real-time analyze vibration data and easily uses without specialist. Furthermore to get better precision we have plan apply to additional machine learning technology.

• 플랜트 저널
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• 제8권3호
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• pp.49-54
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• 2012

현재 운영 중인 발전소 전동기의 고정자 권선 고장 실제사례를 토대로 전동기에서 발생하는 자속 데이터의 고주파 영역 스펙트럼을 분석함으로써 결함이 있는 전동기와 건전한 전동기의 자속 특성 및 변화추이를 분석하였다. 전동기 자속스펙트럼의 고주파 영역을 분석한 결과 결함이 있는 전동기는 고정자 슬롯 주파수 대비 고정자 슬롯 사이드밴드 주파수의 상대적 크기 비율이 건전한 전동기에 비해 매우 높음을 확인하였다. 또한 결함이 있는 전동기는 시간이 지날수록 고정자 슬롯 주파수 대비 고정자 슬롯 사이드밴드 주파수의 상대적 크기 비율 크기도 더욱 커지는 현상도 확인하였다. 그리고 결함이 있는 전동기의 자속 데이터는 불시 고장 1개월여 전부터 결함 상태를 인식할 수 있는 신호를 나타냄으로써 전동기 고정자 권선 결함에 대한 사전 예측능력이 매우 탁월함을 확인하였다.

• Nuclear Engineering and Technology
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• 제53권8호
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• pp.2534-2546
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• 2021

Nuclear emergency preparedness and response is an essential part to ensure the safety of nuclear power plant (NPP). Key support technologies of nuclear emergency decision-making usually consist of accident diagnosis, source term estimation, accident consequence assessment, and protective action recommendation. Source term estimation is almost the most difficult part among them. For example, bad communication, incomplete information, as well as complicated accident scenario make it hard to determine the reactor status and estimate the source term timely in the Fukushima accident. Subsequently, it leads to the hard decision on how to take appropriate emergency response actions. Hence, this paper aims to develop a method for rapid source term estimation to support nuclear emergency decision making in pressurized water reactor NPP. The method aims to make our knowledge on NPP provide better support nuclear emergency. Firstly, this paper studies how to build a Bayesian network model for the NPP based on professional knowledge and engineering knowledge. This paper presents a method transforming the PRA model (event trees and fault trees) into a corresponding Bayesian network model. To solve the problem that some physical phenomena which are modeled as pivotal events in level 2 PRA, cannot find sensors associated directly with their occurrence, a weighted assignment approach based on expert assessment is proposed in this paper. Secondly, the monitoring data of NPP are provided to the Bayesian network model, the real-time status of pivotal events and initiating events can be determined based on the junction tree algorithm. Thirdly, since PRA knowledge can link the accident sequences to the possible release categories, the proposed method is capable to find the most likely release category for the candidate accidents scenarios, namely the source term. The probabilities of possible accident sequences and the source term are calculated. Finally, the prototype software is checked against several sets of accident scenario data which are generated by the simulator of AP1000-NPP, including large loss of coolant accident, loss of main feedwater, main steam line break, and steam generator tube rupture. The results show that the proposed method for rapid source term estimation under nuclear emergency decision making is promising.