• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제53권6호
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• pp.372-379
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• 2004

Most of the loads in industrial power distribution systems are balanced and connected to three power systems. However, in the user power distribution systems, most of the loads are single ＆ three phase and unbalanced, generating voltage unbalance. Rotating machines operating on an unbalanced voltage will draw a highly unbalanced current. As a result, the three-phase currents may differ considerably, thus resulting in an increased temperature rise in the machine. This paper presents a scheme on steady states of a three-phase induction motor under unbalanced voltages. The three-phase voltages applied to the stator winding of the studied induction motor are controlled by respectively adjusting the magnitude and phase angle of each phase. The voltage unbalanced factor(VUF) of the three-phase source voltages can then be varied to examine the different values of VUF on machine's operation characteristics.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 B
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• pp.1522-1524
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• 2004

The wind turbine captures the wind's kinetic energy in a rotor consisting of two or more blades mechanically coupled to an electrical generator. In this paper is proposed models for wind energy power plants using V.A.W.T. and complex concepts using shapes of a half cylinder for blades. A familiar configuration for a drag-type wind machine is shown this paper. In this simple machine, kinetic energy in the wind is converted into mechanical energy in a vertical rotating shaft.

• 한국초전도ㆍ저온공학회논문지
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• 제11권2호
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• pp.33-36
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• 2009

A 1 MW class HTS (High-Temperature Superconducting) synchronous motor has been developed. This motor is aimed to be utilized for industrial application such as large motors operating in large plants. The HTS field coil of the developed motor is cooled by way of neon thermo siphonmechanism and the stator (armature) coil is cooled by water through hollow copper conductor. This paper also describes evaluation of some electrical parameters from performance test results of our motor, which was conducted at steady state in generator mode and motor mode. Open and short circuit tests were conducted in generator mode while a 1.1 MW rated induction machine was rotating the HTS machine. Electrical parameters such as mutual inductance and synchronous inductance are deduced from these tests. Load test was done upto rating torque during motor mode and efficiency was measured at each load torque.

• 한국공작기계학회논문집
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• 제13권3호
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• pp.53-60
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• 2004

We have developed and manufactured an experimental ultrasonic polishing machine with frequency of 20kHz at the power of vibration 1.7㎾ for effective ultrasonic polishing in processing of high hardness material. Design of the horn is performed by the FEM analysis. The following conclusions were empirically deduced through experimental results to clarify the major elements which affect the surface roughness during the ultrasonic process by following the experimental plans. The ultrasonic polishing machine has been developed in parts of structure part, ultrasonic generator, vibrator. We were able to process the high hardness material without difficulty as a result of ultrasonic polishing by utilizing the groove added step-type horn. Through analyzing by applying the experimental plans, the rotating speed of the horn was determined to be the major factor in influencing the surface roughness. In the case of ceramic, wafer, we were able to obtain good surface roughness when the feed rate and the ultrasonic output were higher. Because the load on slurry particle increases when the ultrasonic output is higher, the processed surface becomes worse in the case of optical glass.

• Earthquakes and Structures
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• 제12권6호
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• pp.629-641
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• 2017

Foundation plays a significant role in safe and efficient turbo machinery operation. Turbo machineries generate harmonic load on the foundation due to their high speed rotating motion which causes vibration in the machinery, foundation and soil beneath the foundation. The problems caused by vibration get multiplied if the soil is poor. An improperly designed machine foundation increases the vibration and reduces machinery health leading to frequent maintenance. Hence it is very important to study the soil structure interaction and effect of machine vibration on the foundation during turbo machinery operation in the design stage itself. The present work studies the effect of harmonic load due to machine operation along with earthquake loading on the frame foundation for poor soil conditions. Various alternative foundations like rafts, barrette, batter pile and combinations of barrettes with batter pile are analyzed to study the improvements in the vibration patterns. Detailed computational analysis was carried out in SAP 2000 software; the numerical model was analyzed and compared with the shaking table experiment results. The numerical results are found to be closely matching with the experimental data which confirms the accuracy of the numerical model predictions. Both shake table and SAP 2000 results reveal that combination of barrette and batter piles with raft are best suitable for poor soil conditions because it reduces the displacement at top deck, bending moment and horizontal displacement of pile and thereby making the foundation more stable under seismic loading.

• Journal of Biosystems Engineering
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• 제6권1호
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• pp.48-59
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• 1981

The major limiting factor on the determination of combine capacity is the frequent occurence of clogging over the some parts of machine when the crop is wet in the case of Japanese self-feeding type combine. And in the case of American conventional combine having big separating parts, the great grain loss and damage occur when the machine is used for rice harvesting. This experiment was carried out to develop the new type threshing and separating equipment. Proto-type thresher which consist of a conical threshing drum and a conical separating sieve rotating around the threshing cone was constructed and tested. In the case of 800 rpm of threshing cone speed, average threshing loss was below 1 percent, separating loss was about 1 percent, grain damage was about 0.4 percent, and average total power required was about 2.6 PS. This design has some problems such as higher power required or wrapping problems under the conditions of feeding long damp straw. But, compared with the conventional combine or thresher, this machine certainly has some potentials for this approach to combine development. The crop feed rate must be increased through improvement of the feeding portion of the threshing cone. And it is required to investigate further about some parameters causing wrapping phenomena.

• 한국소음진동공학회논문집
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• 제26권6_spc호
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• pp.714-720
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• 2016

Rubbing and misalignment are the most usual faults that occurs in rotating machinery and with them severe effect on power plant availability. Especially blade rubbing is hard to detect on FFT spectrum using the vibration signal. In this paper, the possibility of feature analysis of vibration signal is confirmed under blade rubbing and misalignment condition. And the lab-scale rotor test device provides the blade rubbing and shaft misalignment modes. Feature selection based on GA (genetic algorithm) is processed by the extracted feature of the time domain. Then, classification of the features is analyzed by using SVM (support vector machine) which is one of the machine learning algorithm. The results of features selection based on GA compared with those based on PCA (principal component analysis). According to the results, the possibility of feature analysis is confirmed. Therefore, blade rubbing and shaft misalignment can be diagnosed by feature of vibration signal.

• 한국기계가공학회지
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• 제20권9호
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• pp.35-41
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• 2021

A rotor is a crucial component in various mechanical assemblies. Additionally, high-speed and high-efficiency components are required in the automotive industry, manufacturing industry, and turbine systems. In particular, the failure of high-speed rotating bearings has catastrophic effects on auxiliary systems. Therefore, bearing reliability and fault diagnosis are essential for bearing maintenance. In this work, we performed failure mode and effect analysis on bearing rotors and determined that corrosion is the most critical failure type. Furthermore, we conducted experiments to extract vibration characteristic data and preprocess the vibration data through principle component analysis. Finally, we applied a machine learning algorithm called support vector machine to diagnose the failure and observed a classification performance of 98%.

• 해양환경안전학회지
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• 제28권2호
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• pp.346-353
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• 2022

최근 회전 회전기계의 건전성 관련 연구가 활발하게 진행중이며, 조선업의 대표적인 회전기계인 갠트리 크레인에도 이를 적용하고자 하는 연구가 활발하게 진행되고 있다. 하지만 조선업의 갠트리 크레인의 경우 상대적으로 낮은 RPM으로 구동되고 잦은 운전과 정지가 이루어지며 충격, 소음 등의 외부환경 인자가 측정 데이터에 영향을 크게 미쳐 오차를 발생시킬 수 있다. 본 연구에서는 조선업의 내업공정에서 사용되는 갠트리 크레인의 Hoist 모사장비를 제작하여, 운전조건(RPM) 변화와 데이터 획득 센서의 위치 차이가 획득 데이터에 미치는 오차를 통계적으로 분석하였다. 연구결과 상대적으로 낮은 운전조건에서는 센서 위치 차이에 따른 획득 데이터의 오차는 크게 발생하지 않았으나, 상대적으로 높은 운전조건에서는 획득 데이터의 오차가 크게 발생하는 것으로 확인하였으며, 회전기계의 데이터 획득 시 운전조건과 획득 센서위치가 획득 데이터에 영향을 미치는 것으로 확인하였다.

• Smart Structures and Systems
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• 제29권6호
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• pp.757-766
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• 2022

To train deep learning algorithms, a sufficient number of data are required. However, in most engineering systems, the acquisition of fault data is difficult or sometimes not feasible, while normal data are secured. The dearth of data is one of the major challenges to developing deep learning models, and fault diagnosis in particular cannot be made in the absence of fault data. With this context, this paper proposes an anomaly detection methodology for rotating machines using only normal data with self-labeling. Since only normal data are used for anomaly detection, a self-labeling method is used to generate a new labeled dataset. The overall procedure includes the following three steps: (1) transformation of normal data to self-labeled data based on a pretext task, (2) training the convolutional neural networks (CNN), and (3) anomaly detection using defined anomaly score based on the softmax output of the trained CNN. The softmax value of the abnormal sample shows different behavior from the normal softmax values. To verify the proposed method, four case studies were conducted, on the Case Western Reserve University (CWRU) bearing dataset, IEEE PHM 2012 data challenge dataset, PHMAP 2021 data challenge dataset, and laboratory bearing testbed; and the results were compared to those of existing machine learning and deep learning methods. The results showed that the proposed algorithm could detect faults in the bearing testbed and compressor with over 99.7% accuracy. In particular, it was possible to detect not only bearing faults but also structural faults such as unbalance and belt looseness with very high accuracy. Compared with the existing GAN, the autoencoder-based anomaly detection algorithm, the proposed method showed high anomaly detection performance.