• Journal of Electrical Engineering and Technology
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• 제9권6호
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• pp.2058-2064
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• 2014

In transformer fault diagnosis, dissolved gas analysis (DGA) is been widely employed for a long period and numerous methods have been innovated to interpret its results. Still in some cases it fails to identify the corresponding faults. Due to the limitation of training data and non-linearity, the estimation of key-gas ratio in the transformer oil becomes more complicated. This paper presents Intuitionistic Fuzzy expert System (IFS) to diagnose several faults in a transformer. This revised approach is well suitable to diagnosis the transformer faults and the corresponding action to be taken. The proposed method is applied to an independent data of different power transformers and various case studies of historic trends of transformer units. It has been proved to be a very advantageous tool for transformer diagnosis and upkeep planning. This method has been successfully used to identify the type of fault developing within a transformer even if there is conflict in the results of AI technique applied to DGA data.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2000년도 추계학술대회논문집 - 한국공작기계학회
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• pp.57-62
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• 2000

Diagnosis or measurements using Infrared thermoimage hasn't been available. A quick diagnosis and thermal analysis can be possible when that kind of system is introduced to the investigation of each part. In this study, Infrared Camera, Thermovision 900 was used in order to investigate. Infrared Camera usually detects only Infrared wave from the light in order to illustrate the temperature distribution. Infrared diagnosis system can be applied to various field. Also, it is more effective to analyze temperature distribution on the welding parts during welding process. Especially, diagnosis using Infrared camera plays an important role on thermal analysis of Axle Casing Nut for Commercial Vehicles.

• 한국임상수의학회지
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• 제1권1호
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• pp.33-40
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• 1984

The number of dairy cows diagnosed and treated at Hyup-dong Animal Hospital from July 1983 to June 1984 was amounted to 3,073 heads of Holstein breed and the result of analysis of diseases was as follows : 1. In the topographic diagnostic categories, 41.23% of all cases involved the urogenital system and were observed most prominent. Next were digestive system (22.10%), general and regional (14.55 %), respiratory system (8.20%) and musculoskeletal system (7.06%) in decreasing order. 2. In urogenital system 50.20% of all diagnosis involved metritis and this were most prominent disease. Next were repeat breeder(15.31%), retained placenta (12.79%) and ovary disfunction(9.40 %) in decreasing order. 3. In digestive system 36.82% of all diagnosis involved enteritis and were most prominent. Next were abomasal displacement (29.31%), lumen indigestion (20.32%) and lumen bloat (9.57%) in decreasing order 4. In general and regional diseases, 52.80% of all diagnosis involved ketosis and were most prominent. Next were milk fever (25.50%), downer cow (5.82%) and omphalitis (4.47%) in decreasing order. 5. In respiratory system, 82.54% of all diagnosis involved upper respiratory tract infections and were most prominent. Next were pneumonia (15.87%). 6. In musculoskeletal system, 49.31% of all diagnosis involved foot rot and were most prominent. 7. In milk secreting system, 61.64% of all diagnosis involved mastitis and ere most prominent. Next were stenosis of teat canal (16.35%). 8. Among the individual disease entities, endometritis marked 20.7%, enteritis 8.13%, ketosis 7.68% upper respiratory tract infection 6.77%, repeat bheeder 6.31%, retained placenta 5.27%, abomasal displacement 4.69%, and lumen impaction 4.49%, respectively of total 3,073 cases in decreasing order.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2004년도 추계학술대회
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• pp.1727-1732
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• 2004

For the purpose of monitoring by ultrasonic test of the ball bearing conditions in rotating machinery, a system for their diagnosis was developed. ultrasonic technique is used to detect abnormal conditions in the bearing system. And various data such as frequency spectrum, energy and amplitude of ultrasonic signals, and ultrasonic parameters were acquired during experiments with the simulated ball bearing system. Based on the above results and practical application for power plant, algorithms and judgement criteria for diagnosis system was established.

• Journal of Electrical Engineering and Technology
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• 제12권2호
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• pp.830-839
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• 2017

Support vector machine (SVM) is introduced as an effective fault diagnosis technique based on dissolved gases analysis (DGA) for oil-immersed transformers with maximum generalization ability; however, the applicability of the SVM is highly affected due to the difficulty of selecting the SVM parameters appropriately. Therefore, a novel approach combing SVM with improved imperialist competitive algorithm (IICA) for fault diagnosis of oil-immersed transformers was proposed in the paper. The improved ICA, which is proved to be an effective optimization approach, is employed to optimize the parameters of SVM. Cross validation and normalizations were applied in the training processes of SVM and the trained SVM model with the optimized parameters was established for fault diagnosis of oil-immersed transformers. Three classification benchmark sets were studied based on particle swarm optimization SVM (PSOSVM) and IICASVM with four multiple classification schemes to select the best scheme for transformer fault diagnosis. The results show that the proposed model can obtain higher diagnosis accuracy than other methods. The comparisons confirm that the proposed model is an effective approach for classification problems.

• 한국소음진동공학회논문집
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• 제21권11호
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• pp.1020-1028
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• 2011

As rotating machines play an important role in industrial applications such as aeronautical, naval and automotive industries, many researchers have developed various condition monitoring system and fault diagnosis system by applying artificial neural network. Since using obtained signals without preprocessing as inputs of neural network can decrease performance of fault classification, it is very important to extract significant features of captured signals and to apply suitable features into diagnosis system according to the kinds of obtained signals. Therefore, this paper proposes a neural-network-based fault diagnosis system using AR coefficients as feature vectors by LPC(linear predictive coding) and EIV(errors-in variables) analysis. We extracted feature vectors from sound, vibration and current faulty signals and evaluated the suitability of feature vectors depending on the classification results and training error rates by changing AR order and adding noise. From experimental results, we conclude that classification results using feature vectors by EIV analysis indicate more than 90 % stably for less than 10 orders and noise effect comparing to LPC.

• 대한유전성대사질환학회지
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• 제6권1호
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• pp.40-51
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• 2006

Purpose: A personal computer-based system was developed for automated metabolic profiling of organic aciduria and aminoacidopathy by gas chromatography-mass spectrometry and data interpretation for the diagnosis of metabolic disorders Methods: For automatic data profiling and interpretation, we compiled retention time, two target ions and their intensity ratio for 77 organic acids and 13 amino acids metabolites. Metabolites above the cut-off values were flagged as abnormal compounds. The data interpretation was a based on combination of flagged metabolites. Diagnostic or index metabolites were categorized into three groups, "and", "or" and "NO" compiled for each disorder to improve the specificity of the diagnosis. Groups "and" and "or" comprised essential and optional compounds, respectively, to reach a specific diagnosis. Group "NO" comprised metabolites that must be absent to make a definite diagnosis. We tested this system by analyzing patients with confirmed Propionic aciduria and others. Results: In all cases, the diagnostic metabolites were identified and correct diagnosis was founded to be made among the possible disease suggested by the system. Conclusion: The study showed that the developed method could be the method of choices in rapid, sensitive and simultaneous screening for organic aciduria and amino acidopathy with this simplified automated system.

• 한국콘텐츠학회논문지
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• 제17권9호
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• pp.466-475
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• 2017

스트레스는 매일 살아가면서 사소한 일부터 큰 충격을 받는 삶의 주요한 사건까지 다양한 요인으로부터 온다. 이는 어느 한 시기에만 나타나는 것이 아니라, 생애에 거쳐 나타나므로 지속적으로 스트레스를 관리할 수 있는 연구가 필요하다. 본 연구에서는 사용자 스스로 스트레스를 진단, 관리할 수 있는 홍채 분석기반 시스템을 개발하였다. 그리고 홍채진단에 의한 스트레스 지수와 설문기반의 스트레스 지수의 상관성을 분석한 후, 사용자의 스트레스 지수를 정량화하였다. 본 연구에서 제안하는 시스템은 사용자가 쉽게 현재 자신이 느끼고 있는 스트레스 정도를 알 수 있고, 이에 상응하여 예측되는 질병에 대한 조기 진단 및 질병예방의 하나의 방법으로 사용할 수 있을 것이다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2003년도 춘계학술대회 논문집
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• pp.430-433
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• 2003

Air-operated valve is one of principal valves that are using to control fluid flow. A period diagnosis for safety of power plants is necessary. But there are many difficulties such as economic loss caused by intone of high cost devices and a matter hard to deal with users. In this study we developed the diagnosis system that usersofpower plants are easy to handle. The diagnosis system is composed of database module, reliability analysis module, design safety nodule and diagnosis test and evaluation module.

• Journal of Advanced Marine Engineering and Technology
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• 제20권1호
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• pp.13-23
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• 1996

Weld defects degrade the strength and safety of astructure and are resulted from the various cases. The complexity of causal relation of weld defects requires an expert for the analysis of weld defects and the measures counter to them. An expert system has the intelligent functions such as the representation of knowledge and the inference. On this research, weld defect are systematically analysed and their causal model is developed. This information is saved to the knowledge base. The suitable inference algorithm for the diagnosis of weld defects is developed and realized with C++ programming.