• 유공압시스템학회논문집
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• 제8권3호
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• pp.1-7
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• 2011

As the technologies of electronic device have advanced these days, most of mechanical systems are designed with electronic control unit to take advantage of control parameter adaption to operating conditions and firmware flexibilities as well. On-board diagnosis, which detects the system malfunction and identifies potential source of error with its own diagnostic criteria, and fail-safe that can switch the mode of operation in view of recognized error characteristics enables easy maintenance and troubleshooting as well as system protection. This paper dealt with the development of diagnosis and fail-safe function for proportional flow control valve. All type of errors related to valve control system components are investigated and assigned to a specific hexadecimal codes. Cumulative error detection algorithm is applied in order for the sensitivity and reliability to be appropriate. Embedded simulator which runs simultaneously with system program provides the virtual error simulation environment for expeditious development of error detection algorithm. The diagnosis function was verified both with solenoid valve and embedded simulator test and it will enhance the valve control system monitoring function.

• Nuclear Engineering and Technology
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• 제32권3호
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• pp.261-273
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• 2000

During an extensive review made as part of the Integrated Diagnosis System project of the Maiden Reactor Project, MOAS (Maryland Operator Advisory System) was identified as one of the most thorough systems developed thus far. MOAS is an integrated on-line diagnosis system that encompasses diverse functional aspects that are required for an effective process disturbance management: (1) intelligent process monitoring and alarming, (2) on-line sensor data validation and sensor failure diagnosis, (3) on-line hardware (besides sensors) failure diagnosis, and (4) real-time corrective measure synthesis. The MOAS methodology was used at the Maiden Man-Machine Laboratory HAMMLAB of the OECD Maiden Reactor Project. The performance of MOAS, developed in G2 real-time expert system shell for the high-pressure preheaters of the NORS process in the HAMMLAB, was tested against a variety of transient scenarios, including failures of the control valves and sensors, and tube leakage of the preheaters. These tests showed that MOAS successfully carried out its intended functions, i.e., quickly recognizing an occurring disturbance, correctly diagnosing its cause, and presenting advice on its control to the operator. The lessons learned and insights gained during the implementation and performance tests also are discussed.

• 한국조명전기설비학회지:조명전기설비
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• 제9권2호
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• pp.68-78
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• 1995

지금까지 공동주택에서 전력감시반은 지하실에 설치된 그래픽보드를 이용하여 감시를 수행하고 있으나, 공동주택에서의 인력구조와 시스템특성상 적절한 감시방법이라고 할 수 없다. 따라서 본 논문에서는 컴퓨터를 이용한 인텔리전트 감시시스템을 제안하였다. 제안된 감시반은 직렬통신방식을 이용한 집중감시시스템으로 설비를 디자인된 화면으로 실시간 감시하고 사용이력과 고장진단을 위한 지식베이스를 구현하여 감시반 효율성의 향상과 감시반의 공사비 절감을 유도하였으며, 실시간 O.S는 Trend 940을 이용하였다.

• 전기학회논문지
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• 제63권7호
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• pp.945-949
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• 2014

In cardiac magnetic resonance imaging (CMRI), heart and respiratory motions are one of main obstacles in obtaining diagnostic quality of images. To synchronize CMRI to the physiological motions, ECG and respiratory gatings are commonly used. In this paper multi-biological signal (ECG, respiratory, and SPO2) based smart trigger system is proposed. By using multi-biological signal, the proposed system is robust to the induced noise such as eddy current when gradient pulsing is continuously applied during the examination. Digital conversion of the multi-biological signal makes the system flexible in implementing smart and intelligent algorithm to detect cardiac and respiratory motion and to reject arrhythmia of the heart. The digital data is used for real-time trigger, as well as signal display, and data storage which may be used for retrospective signal processing.

• Smart Structures and Systems
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• 제17권1호
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• pp.135-147
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• 2016

This paper describes a smart structural system, which uses smart materials for real-time monitoring and active control of bolted-joints in steel structures. The goal of this research is to reduce the possibility of failure and the cost of maintenance of steel structures such as bridges, electricity pylons, steel lattice towers and so on. The concept of the smart structural system combines impedance based health monitoring techniques with a shape memory alloy (SMA) washer to restore the tension of the loosened bolt. The impedance-based structural health monitoring (SHM) techniques were used to detect loosened bolts in bolted-joints. By comparing electrical impedance signatures measured from a potentially damage structure with baseline data obtained from the pristine structure, the bolt loosening damage could be detected. An outlier analysis, using generalized extreme value (GEV) distribution, providing optimal decision boundaries, has been carried out for more systematic damage detection. Once the loosening damage was detected in the bolted joint, the external heater, which was bonded to the SMA washer, actuated the washer. Then, the heated SMA washer expanded axially and adjusted the bolt tension to restore the lost torque. Additionally, temperature variation due to the heater was compensated by applying the effective frequency shift (EFS) algorithm to improve the performance of the diagnostic results. An experimental study was conducted by integrating the piezoelectric material based structural health monitoring and the SMA-based active control function on a bolted joint, after which the performance of the smart 'self-monitoring and self-healing bolted joint system' was demonstrated.

• Nuclear Engineering and Technology
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• 제50권8호
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• pp.1306-1313
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• 2018

The randomness and incipient nature of certain faults in reactor systems warrant a robust and dynamic detection mechanism. Existing models and methods for fault diagnosis using different mathematical/statistical inferences lack incipient and novel faults detection capability. To this end, we propose a fault diagnosis method that utilizes the flexibility of data-driven Support Vector Machine (SVM) for component-level fault diagnosis. The technique integrates separately-built, separately-trained, specialized SVM modules capable of component-level fault diagnosis into a coherent intelligent system, with each SVM module monitoring sub-units of the reactor coolant system. To evaluate the model, marginal faults selected from the failure mode and effect analysis (FMEA) are simulated in the steam generator and pressure boundary of the Chinese CNP300 PWR (Qinshan I NPP) reactor coolant system, using a best-estimate thermal-hydraulic code, RELAP5/SCDAP Mod4.0. Multiclass SVM model is trained with component level parameters that represent the steady state and selected faults in the components. For optimization purposes, we considered and compared the performances of different multiclass models in MATLAB, using different coding matrices, as well as different kernel functions on the representative data derived from the simulation of Qinshan I NPP. An optimum predictive model - the Error Correcting Output Code (ECOC) with TenaryComplete coding matrix - was obtained from experiments, and utilized to diagnose the incipient faults. Some of the important diagnostic results and heuristic model evaluation methods are presented in this paper.

• 지능정보연구
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• 제1권1호
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• pp.91-109
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• 1995

This paper compared four knowledge acquisition methods (namely, neural network, case-based reasoning, discriminant analysis, and covariance structure modeling) for allergic rhinitis. The data were collected from 444 patients with suspected allergic rhinitis who visited the Otorlaryngology Deduring 1991-1993. Among four knowledge acquisition methods, the discriminant model had the best overall diagnostic capability (78%) and the neural network had slightly lower rate(76%). This may be explained by the fact that neural network is essentially non-linear discriminant model. The discriminant model was also most accurate in predicting allergic rhinitis (88%). On the other hand, the CSM had the lowest overall accuracy rate (44%) perhaps due to smaller input data set. However, it was most accuate in predicting non-allergic rhinitis (82%).

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2006년도 제27회 추계학술대회논문집
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• pp.281-284
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• 2006

PW206C 터보 축 엔진을 위해 신경회로망을 이용한 지능형 성능 진단 프로그램이 제안되었다. 이 엔진은 항공우주연구원에서 개발 중에 있는 틸트 로터 타입 스마트 무인기의 추진시스템으로 선정되었다. 스마트 무인기 추진시스템에서 계측되는 성능변수는 가스발생기 회전속도, 동력터빈 회전속도, 배기가스 온도, 토크 등 4개이다. 그러나 이러한 4개 계측변수로는 각 구성품의 손상 진단이 어려운 관계로 압축기 출구 압력 및 압축기 터빈 출구 온도를 포함한 6개의 계측변수를 진단에 이용하였다. 구성품 성능저하량을 판단하는 성능파라미터는 압축기, 압축기 터빈, 동력 터빈의 유량함수 및 효율이다. 신경망을 훈련하고 테스트하기 위한 데이터베이스는 가스터빈 성능모사 프로그램을 이용하여 구성하였다. 훈련된 신경망을 PW206C 터보 축 엔진의 진단에 적용한 결과 제안된 진단 알고리즘이 압축기 오염과 압축기 터빈의 침식과 같은 단일 손상을 탐지하는데 유용함을 확인하였다.

• 한국융합학회논문지
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• 제6권5호
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• pp.33-38
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• 2015

V2X 카 융합서비스 환경에서, 인포테인먼트 서비스와 운전자 관리 서비스 가운데 주요 서비스는 드라이버, 유지보수 관리자, 고객, 익명의 사용자의 중요한 정보를 중심으로 지원되어야 한다. 많은 소프트웨어 어플리케이션들이 운전 관리 프로그램과 계획의 특정 요구조건을 만족하기 위해 솔루션을 고려해오고 있다. 본 논문에서는 V2X 융합서비스 환경에서 클리닉 환경설정, 클리닉, 클리닉 페이지, 맴버십, 클리닉 요청 처리, 운전자 프로파일 데이터, 클리닉 맴버십 데이터 그리고 클리닉 인증을 포함한 운전자용 차량 진단을 위한 안전한 관리 시스템의 Data flow diagram을 설명하였다. STRIDE 모델 가운데 스푸핑, 탬퍼링, 부인방지, 노출, 서비스 거부, 권한 관리와 같은, ITS 진단 시스템의 보안 위협 이슈를 중심으로 고찰하였다.

• 한국컴퓨터정보학회논문지
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• 제28권10호
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• pp.1-8
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• 2023

자동차의 주요 부품인 휠 베어링에 결함이 생기면 교통사고등 문제를 발생시켜 이를 해결하기 위해 빅데이터를 수집해서 예측진단 및 관리 기술을 통한 휠 베어링의 고장 유무 및 고장 유형을 조기에 알려 주는 알고리즘과 모니터링 시스템 개발이 필요하다. 본 논문에서는 이러한 지능형 휠 허브 베어링 정비 시스템 구현을 위해 신뢰성 및 건전성에 대한 모니터링용 센서 및 예측 진단하는 알고리즘이 탑재된 임베디드 시스템을 개발하였다. 사용된 알고리즘은 휠 베어링에 설치된 가속도 센서로부터 진동 신호를 취득하고 이를 신호 처리기법, 결함주파수 분석, 건전성 특징 인자정의 등의 과정을 빅데이터 기술을 통해 고장을 예측하고 진단할 수 있다. 구현된 알고리즘은 진동 주파수 성분들은 최소화하고 휠 베어링에서 발생하는 진동 성분을 극대화할 수 있는 안정 신호 추출 알고리즘을 적용하고, 필터를 활용한 노이즈 제거에서는 인공지능 기반의 건전성 추출 알고리즘을 적용하였으며, FFT를 통한 결함 주파수를 분석하여 고장 특성인자 추출을 통한 고장을 진단하였다. 본 시스템의 성능 목표는 12,800ODR 이상으로 시험 결과를 통해 목표치를 만족하였다.