• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.04a
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• pp.417-419
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• 2014

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.10a
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• pp.316-317
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• 2013

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.2
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• pp.104-115
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• 2002

Core is the main component of inductor. This core should be classified into around 10 classes according to the value of inductance and Q. The coil should be winded with the outer-boundary of this core by different number of turns. Theses kind of precise inductors would be required in the future environment which PCs and communication devices demand more high speed and lower voltage level. It would be quite unefficient that only one core is classified once a time. There, it will be developed so that 10 cores are classified simultaneously. For the operation of classifying 10 cores once in a time, suppose 10 test instruments could be used. In this case, it would take much cost since a test instrument Is expensive. So, by using only one test instrument, it is really more desirable that this system is developed. Each core classified by 10 different classes is to be stored into the corresponding box through the corresponding rubber hose. 10 cores are passed on a serial line and are placed on each testing slot. Here, each core located at each slot is tested, and then the bowl located on the top of a step motor is moved into the corresponding spot by rotating step motor with some angles. Each bowl connected with the corresponding box through rubber hose. Actually 100 hoses are connected, 10 step motors are rotated at 10 different angles, so the size is really so big, the shape of connecting 100 hoses is so complicated. Therefore it is anticipated that the system would be going to be easily out of ordered. In this paper the main purpose is to make several suggestions to be able to work well in these kinds of being affected by the abnormal operation of motors and the flow of cores.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.4
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• pp.551-559
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• 2017

In this paper, a real-time training method to improve the performance of bearing fault diagnosis. The traditional bearing fault diagnosis cannot classify a condition which is not trained by the classifier. The proposed 4-step method trains and recognizes new condition in real-time, thereby it can classify the condition accurately. In the first step, we calculate the maximum distance value for each class by calculating a Euclidean distance between a feature vector of each class and a centroid of the corresponding class in the training information. In the second step, we calculate a Euclidean distance between a feature vector of new acquired data and a centroid of each class, and then compare with the allowed maximum distance of each class. In the third step, if the distance between a feature vector of new acquired data and a centroid of each class is larger than the allowed maximum distance of each class, we define that it is data of new condition and increase count of new condition. In the last step, if the count of new condition is over 10, newly acquired 10 data are assigned as a new class and then conduct re-training the classifier. To verify the performance of the proposed method, bearing fault data from a rotating machine was utilized.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.341-344
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• 2004

발전소에서 운전 중인 발전 설비의 장비 및 기계의 동작, 감시, 진단은 매우 중요한 일이다. 발전소의 이상 감지를 위해 상태 모니터링이 사용되며, 이상이 발생되었을 때 고장의 원인을 분석하고 적절한 조치를 계획하기 위한 이상 진단 과정을 따르게 된다. 본 논문에서는 산업 현장에서 기기들의 운전시에 발생하는 기기 발생 음을 획득하여 정상/비정상을 판정하기 위한 알고리듬에 대하여 연구하였다. 사운드 감시(Sound Monitoring) 기술은 관측된 신호를 acoustic event로 분류하는 것과 분류된 이벤트를 정상 또는 비정상으로 구분하는 두 가지 과정으로 진행할 수 있다. 기존의 기술들은 주파수 분석과 패턴 인식의 방법으로 간단하게 적용되어 왔으며, 본 논문에서는 K-means clustering 알고리듬을 이용하여 사운드를 acoustic event로 분류하고 분류된 사운드를 정상 또는 비정상으로 구분하는 알고리듬을 개발하였다.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.20 no.7
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• pp.508-516
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• 2008

Fault detection and diagnosis(FDD) system is beneficial in equipment management by providing the operator with tools which can help find out a failure of the system. An experimental study has been performed on fault detection and diagnosis method for a water chiller. Bayes classifier, which is one of classical pattern classifiers, is adopted in deciding whether fault occurred or not. Failure modes in this study include refrigerant leakage, decrease in mass flow rate of the chilled water and cooling water, and sensor error of the cooling water inlet temperature. It is possible to detect and diagnose faults in this study by adopting FDD algorithm using only four parameters(compressor outlet temperature, chilled water inlet temperature, cooling water outlet temperature and compressor power consumption). Refrigerant leakage failure is detected at 20% of refrigerant leakage. When mass flow rate of the chilled and cooling water decrease more than 8% or 12%, FDD algorithm can detect the faults. The deviation of temperature sensor over $0.6^{\circ}C$ can be detected as fault.

• Proceedings of the KSR Conference
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• 2008.11b
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• pp.1473-1480
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• 2008

This article deals with a case study on evaluating RAM(Reliability, Availability and Maintainability) performance of the door systems. The RAM performance is analysed based on the field data. The definition and category of failures of the door systems are presented and reliability for each categories is predicted with field data. MTTR(Mean Time To Repair) is estimated through the replacement time of line replaceable units(LRU). The availability is predicted based on the estimated reliability and maintainability.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.3
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• pp.1-7
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• 2021

With the advent of industry 4.0, big data and machine learning techniques are being widely adopted in the maintenance domain. Inverters are widely used in many engineering applications. However, overloading and complex operation conditions may lead to various failures in inverters. In this study, failure mode effect analysis was performed on inverters and voltages collected to investigate the over-voltage effect on capacitors. Several features were extracted from the collected sensor data, which indicated the health state of the inverter. Based on this correlation, the best features were selected for classification. Moreover, random forest classifiers were used to classify the healthy and faulty states of inverters. Different performance metrics were computed, and the classifiers' performance was evaluated in terms of various health features.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2019.05a
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• pp.230-231
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• 2019

This study is a development on condition based maintenance(CBM) technology which is a core item of future autonomous ships. It is developing to design & installation of condition monitoring system and acquisition & processing of data from ongoing ships for fault prediction & prognosis of engine in operation. The ultimate goal of this study is to develop a predicts and decision support software for marine engine faults. To do this, the FMEA and fault tree analysis of the main engine should be accompanied by the analysis of classification of system, identification of the components, the type of faults, and the cause and phenomenon of the failure. Finally, the CBM system solution software could predict and diagnose the failure of main engine through integrated analysis for bid-data of ongoing ships and engineering knowledge. Through this study, it is possible to pro-actively cope with abnormal signals of engine and to manage efficiently, and as a result, expected that marine accident and ship operation loss during navigation will be prevented in advance.

• Journal of Korean Society of Environmental Engineers
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• v.35 no.11
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• pp.763-768
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• 2013

The simulation investigation on the availability with failure density function of major equipment for a sewage treatment plant has been carried out. This study focuses on the availability of the plant and criticality with equipment module induced by component layout and its failure function. The equipment classification of sewage treatment plant and its failure function are established. Also solution methodologies are introduced as Monte-Carlo simulation method and event algorithm for uncertainty problem. The availability in the case of serial connection of equipment with all exponential function is calculated as around 50.4%. In other case of parallel combination with back up equipment, the availability showed over 80.1%. The criticality that a ffects availability showed high value over 77% in the dehydration and concentration process of sludge.