• Journal of Electrical Engineering and Technology
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• 제13권6호
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• pp.2392-2401
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• 2018

Creepage discharge faults in air on solid insulating material play a vital role in degradation and ageing of material which ultimately leads to breakdown of power equipment. And electric discharge decompose air in to its by-products such as Ozone and $NO_x$ gases. By analyzing air decomposition gases is a potential method for fault diagnostic in air. In this paper, experimental research has been conducted to study the effect of creepage discharge on rate of generation of air decomposition by-products using different insulating materials such as RTV, epoxy and fiberglass laminated sheet. Moreover XRF analysis has been done to analyze creepage discharge effect on these insulating materials. All experiments have been done in an open air test cell under constant temperature and pressure conditions. While analysis has been made for low and high humidity conditions. The results show that the overall concentration of air decomposition by-products under creepage discharge in low humidity is 4% higher than concentration measured in high humidity. Based on this study a mathematical relationship is also proposed for the rate of generation of air decomposition by-products under creepage discharge fault. This study leads to indirect way for diagnostic of creepage discharge propagation in air.

• 한국추진공학회지
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• 제25권2호
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• pp.88-97
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• 2021

가스터빈엔진의 제어기는 수출입 규제로 인하여 엔진 제작사로부터 기술이전이 불가능하여 가스터빈엔진의 독자개발을 위하여 자체 개발이 필요한 분야이다. 한국항공우주연구원에서는 엔진제어로직연구의 일환으로 소형 가스터빈엔진을 활용하여 고장탐구 연구를 진행하였다. 엔진의 지상 시험설비를 활용하여 정상상태에서의 엔진의 거동 및 성능을 분석한 후, 제어로직 분석시험 환경을 구축하여 엔진의 각종 고장을 모사한 후, 고장이 발생하였을 때, 해당 엔진이 정상상태와 어떻게 다르게 거동하는지 파악하고 이에 대하여 정리하였다. 이를 통하여 향후 엔진 제어기 관련 연구에서 엔진의 각종 이상 상태 발생 시의 제어로직 연구를 수행하는 데 있어 배경지식을 제공하고자 하였다.

• Nuclear Engineering and Technology
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• 제28권1호
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• pp.36-43
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• 1996

본 논문은 원자력발전소 열성능 감시 시스템의 PC기반 구현에 관한 연구 내용이다. 이 시스템은 열성능 감시와 진단을 플랜트 운전중에 실시간으로 수행할 수 있다. 고리 원전2호기를 목적호기로 원형 시스템을 구성하여 시험해 보았다. 원자력발전소의 열 주기 시스템은 대단히 복잡하고 구성 요소간에 상호 영향이 커서, 그 분석과 고장 진단에 어려움이 많다. 본 연구에서는 열 주기를 효율적으로 표현하고, 계산시간을 단축하기 위해 성능 진단 변수를 설정하였다. 비정상 상태에서의 진단 변수의 특성 패턴 변화를 인식하기 위해 자기학습 신경망의 일종인 퍼지아트맵을 이용하였다. 시험을 통해 이 알고리듬이 비정상 상태를 감지하고 고장 원인을 성공적으로 규명하는 것을 보였으며, 운전원의 편의를 위해 그래픽 사용자 인터페이스를 구축하였다.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2006년도 추계학술대회논문집
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• pp.411-416
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• 2006

Fault detection and diagnosis is the most important technology in condition-based maintenance(CBM) system that usually begins from collecting signatures of running machines using multiple sensors for subsequent accurate analysis. With the quick development in industry, there is an increasing requirement of selecting special sensors that are cheap, robust, and easy-installation. This paper experimentally investigated performances of four types of sensors used in induction motors faults diagnosis, which are vibration, current, voltage and flux. In addition, diagnostic effects of five popular classifiers also were evaluated. First, the raw signals from the four types of sensors are collected at the same time. Then the features are calculated from collected signals. Next, these features are classified through five classifiers using artificial intelligence techniques. Finally, conclusions are given based on the experiment results.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2000년도 춘계학술대회논문집 - 한국공작기계학회
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• pp.20-27
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• 2000

A chatter vibration and a workpiece burn are the main phenomena to be monitored in modern grinding processes. This study describes a trouble diagnosis of the cylindrical plunge grinding process using the power and acoustic emission (AE) signals. The raw signals of the power and the AE occurred during the grinding operation were sampled and analyzed to determine the relationship between each fault and change of signals. A neural network that has a high success rate of the fault detection was used. Furthermore, an analysis on the influence of parameters to the chatter vibration and the grinding burn was conducted.

• 한국안전학회지
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• 제26권2호
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• pp.20-25
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• 2011

The fire event occurred in fire proof zone often causes serious electrical problems such as shorts, ground faults, or open circuits in nuclear power plants. These would be directed to the loss of safe shutdown capabilities performed by safety related systems and equipments. The fire event can treat the basic design principle that safety systems should keep their functions with redundancy and independency. In case of a multi-core cable fire, operators can not perform their mission properly and can misjudge the situation because of spurious operation, wrong indication or instrument. These would deteriorate the plant capabilities of safety shutdown and make disastrous conditions. In this paper, the characteristic of cable fire is investigated and the heat-flux evaluation for cable fire is studied. Moreover, a diagnostic methodology for degraded cable in nuclear power plants is presented.

• Nuclear Engineering and Technology
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• 제36권3호
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• pp.219-228
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• 2004

[1] and [2] present an approach to diagnosing possible defects in the mechanical systems of a nuclear power plant. In this paper, by using a fault library as a database and training data, we develop a diagnostic algorithm 1) to decide whether an Air Operated Valve system is sound or not and 2) to identify the defect from which an Air-Operated Valve system suffers, if any. This algorithm is composed of three stages: a neural net stage, a non-neural net stage, and an integration stage. The neural net stage is a simple perceptron, a pattern-recognition module, using a neural net. The non-neural net stage is a simple pattern-matching algorithm, which translates the degree of matching into a corresponding number. The integration stage collects each output and makes a decision. We present a simulation result and confirm that the developed algorithm works accurately, if the input matches one in the database.

• 한국항공운항학회지
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• 제16권2호
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• pp.1-11
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• 2008

Increasing demand for improved reliability and survivability of mission-critical systems is driving the development of health monitoring and Automated Contingency Management (ACM) systems. An ACM system is expected to adapt autonomously to fault conditions with the goal of still achieving mission objectives by allowing some degradation in system performance within permissible limits. ACM performance depends on supporting technologies like sensors and anomaly detection, diagnostic/prognostic and reasoning algorithms. This paper presents the development of a generic prototype test bench software framework for developing and validating ACM systems for advanced propulsion systems called the Propulsion ACM (PACM) Test Bench. The architecture has been implemented for a Monopropellant Propulsion System (MPS) to demonstrate the validity of the approach. A Simulink model of the MPS has been developed along with a fault injection module. It has been shown that the ACM system is capable of mitigating the failures by searching for an optimal strategy. Furthermore, the concepts of Validation and Verification (V&V) of such systems are introduced with relevant examples.

• 한국안전학회지
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• 제31권3호
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• pp.28-33
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• 2016

Since defects in the wheels of railway vehicles, which occur due to wears with the rail, cause serious damage to the running device, the diagnostic monitoring system for condition-based maintenance is required to secure the driving safety. In this paper, we studied to apply a useful Cepstrum analysis to detect periodic structure in spectrum among the vibration signal processing techniques for the fault diagnosis of a rotating body such as wheel. In order to analyze in variations of train velocity, the Cepstrum analysis was performed after a domain change of the vibration signal from time domain to rotation angle domain. When domains change, it is important to use a interpolation for a uniform interval of the rotation angle. Finally, the Cepstrum analysis for wheel flat detection was verified by using the vibration signal including the disturbance resulting from the rail irregularities and the vibration of bogie components.

• 한국정보전자통신기술학회논문지
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• 제12권3호
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• pp.242-250
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• 2019

본 논문에서는 인공신경망(Artificial Neural Network) 모델 중, 시계열 데이터의 변환을 위한 모델인 Seq2Seq(Sequence to Sequence) 모델을 이용한 산업용 로봇 고장 예지 기술에 대하여 제안한다. 제안 방법은 고장 예지를 위한 추가적인 센서의 부착 없이 로봇 자체적으로 측정 가능한 관절 별 전류와 각도 값을 데이터로 사용하였고, 측정된 데이터를 모델이 학습할 수 있도록 전처리한 후, Seq2Seq 모델을 통해 전류를 각도로 변환하도록 지도 학습 하였다. 고장 진단을 위한 이상 정도(Abnormal degree)는 예측 각도와 실제 각도 간의 단위시간 동안의 RMSE(Root Mean Squared Error)를 사용하였다. 제안 방법의 성능평가는 로봇의 정상 및 결함 조건을 달리한 상태에서 측정한 테스트 데이터를 이용하여 수행되었고 이상 정도가 임계값 넘어가면 고장으로 분류하게 하여, 실험으로부터 96.67% 고장 진단 정확도를 보였다. 제안 방법은 별도의 추가적인 센서 없이 고장 예지 수행이 가능하다는 장점이 있으며, 로봇에 대한 깊은 전문지식을 요구하지 않으면서 수행할 수 있는 방법으로 높은 진단 성능과 효용성을 실험으로부터 확인하였다.