• 조명전기설비학회논문지
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• 제27권3호
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• pp.65-78
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• 2013

To analyze influences under open fault conditions in switching devices, an integrated simulation and condition monitoring scheme for a permanent magnet synchronous generator (PMSG) based variable speed grid-connected wind turbine system are presented. Among various faults in power electronics components, the open fault in switching devices may arise when the switches are destructed by an accidental over current, or a fuse for short protection is blown out. Under such a faulty condition, the grid-side inverter as well as the generator-side converter does not operate normally, producing an increase of current harmonics, and a reduction in output and efficiency. As an effective way for a condition monitoring of generation system by online basis without requiring any diagnostic apparatus, the estimation schemes for generated voltage, flux linkage, and stator resistance are proposed and the validity of the proposed scheme is proved through comparative simulations.

• 한국가스학회지
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• 제4권4호
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• pp.42-49
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• 2000

대부분의 화학공정은 매우 크고 복잡하기 때문에 전체 공정에 대한 진단시스템을 만드는 것은 매우 어렵다. 따라서, 대형공정을 몇 개의 부공정으로 분할하여 진단하는 체계적인 방법이 필요하다. 이 논문에서는 이상-결과 트리모델에 기반하여 정성적 이상진단을 위한 공정분할전략을 제안하였다. 분할기준으로 유연한 진단, 지식베이스의 크기축소, 및 복잡한 지식베이스의 일관된 구축을 사용하였다 부공정간의 인과관계를 연결하기 위해 통로변수를 도입한 다음 오프라인 분석을 통해 통로변수의 이상-결과 트리모델을 구축하였다 계분할이 없는 경우와 같은 진단결과를 얻을 수 있도록 온라인 진단전략을 수립하였다 제안된 방법의 유용성을 대형 보일러 공정에 대한 이상진단시스템을 통해 보였다.

• 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.

• 대한기계학회논문집B
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• 제20권6호
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• pp.2057-2066
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• 1996

^In this paper, diagnostic technique for detecting the engine faults, especially misfire, are introduced and compared with each other under the same conditions. With all of them the instantaneous angular velocitys, measured at the flywheel, were analyzed. The techniques include the frequency analysis, auto-correlation function, velocity index, acceleration index, maximum acceleration index, and integrated torque index. Since the main driving components for the angular velocity fluctuation are both the pressure and the inertia torque, the component of the inertia torque in it must be excluded to extract the information of the combustion from the angular velocity. To do this, it is required to consider only the first half of the combustion period in the angular velocity fluctuations, which has never been proposed in the existing methods. On the basis of this fact, the results show that the most effective diagnostic technique is maximum acceleration index.

• Nuclear Engineering and Technology
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• 제55권2호
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• pp.493-505
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• 2023

Comprehensive condition monitoring of large industry systems such as nuclear power plants (NPPs) is essential for safety and maintenance. In this study, we developed novel system-scale diagnostic technology based on deep-learning and IR thermography that can efficiently and cost-effectively classify system conditions using compact Raspberry Pi and IR sensors. This diagnostic technology can identify the presence of an abnormality or accident in whole system, and when an accident occurs, the type of accident and the location of the abnormality can be identified in real-time. For technology development, the experiment for the thermal image measurement and performance validation of major components at each accident condition of NPPs was conducted using a thermal-hydraulic integral effect test facility with compact infrared sensor modules. These thermal images were used for training of deep-learning model, convolutional neural networks (CNN), which is effective for image processing. As a result, a proposed novel diagnostic was developed that can perform diagnosis of components, whole system and accident classification using thermal images. The optimal model was derived based on the modern CNN model and performed prompt and accurate condition monitoring of component and whole system diagnosis, and accident classification. This diagnostic technology is expected to be applied to comprehensive condition monitoring of nuclear power plants for safety.

• 전기학회논문지
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• 제61권11호
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• pp.1758-1764
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• 2012

A large number of wire rope has been used in various inderstiries as Cranes and Elevators from expanding the scale of the industrial market. But now, the management of wire rope is used as manually operated by rope replacement from over time or after the accident.It is caused to major accidents as well as economic losses and personal injury. Therefore its time to need periodic fault diagnosis of wire rope or supply of real-time monitoring system. Currently, there are several methods has been reported for fault diagnosis method of the wire rope, to find out the feature point from extracting method is becoming more common compared to time wave and model-based system. This method has implemented a deterministic modeling like the observer and neural network through considering the state of the system as a deterministic signal. However, the out-put of real system has probability characteristics, and if it is used as a current method on this system, the performance will be decreased at the real time. And if the random noise is occurred from unstable measure/experiment environment in wire rope system, diagnostic criterion becomes unclear and accuracy of diagnosis becomes blurred. Thus, more sophisticated techniques are required rather than deterministic fault diagnosis algorithm. In this paper, we developed the fault diagnosis of the wire rope using probability density estimation techniques algorithm. At first, The steady-state wire rope fault signal detection is defined as the probability model through probability distribution estimate. Wire rope defects signal is detected by a hall sensor in real-time, it is estimated by proposed probability estimation algorithm. we judge whether wire rope has defection or not using the error value from comparing two probability distribution.

• 조명전기설비학회논문지
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• 제18권6호
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• pp.70-77
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• 2004

산업 응용분야에서 유도전동기 구동시스템의 예상치 않은 고장은 전체 계통의 정지, 막대한 손실 등을 가져올 수 있다. 이러한 문제점을 해결하는 방법 중에 하나로서 본 논문은 유도전동기 구동을 위한3상 전압형 PWM 인버터에 개방-스위치 손상의 고장진단에 대하여 연구한다. 고장진단 방법으로는, 먼저 고장의 특징추출을 위하여 3상 전류를 d-q 전류로 변환한 후 평균 전류벡터를 구한다. 다음으로 여러 종류의 고장 패턴을 진단하기 위하여 한 인공지능 알고리즘을 제안한다. 제안된 기법은 일반적인 뉴로-퍼지 시스템(adaptive neuro-fuzzy algorithm)의 전제 부에 클러스터링을 도입한 기법으로 적은 계산 양과 좋은 성능을 갖는다. 최종적으로, 여러 불확실한 요소를 가진 고장계통에 대하여 제안된 알고리즘의 유용성을 모의실험에 의해 검증하였다.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 추계학술대회논문집
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• pp.770-773
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• 2007

As higher reliability and accuracy on production facilities are required to detect incipient faults, a diagnostic system for predictive maintenance of the facility is highly recommended. In this paper, it presents a study on the application of vibration signals to diagnose faults for a Rotating Machinery using the Mahalanobis Distance-Taguchi System. RMS, Crest Factor and Kurtosis that is known as the Statistical Methods and the spectrum analysis are used to diagnose faults as parameters of Mahalanobis distance.

• 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.