• 한국정보통신학회논문지
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• 제7권6호
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• pp.1291-1296
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• 2003

본 연구는 3상 소형 유도전동기의 고정자 권선 고장의 효과적인 진단을 위하여 고정자 전류에 대하여 Park's Vector 기법을 적용하였다. 본 기법은 고정자 3상 전류를 측정하여 Park's vector 변환을 통하여 직축, 횡축 전류로 변환하고, 이를 이용하여 고장 진단을 위한 Park's Vector Pattern을 얻어 정상 상태 패턴과 고장 권선 패턴을 비교하였다. 고정자 권선 한 상에 2턴, 10턴, 그리고 20 턴의 단락고장을 발생시켜 정격부하의 25%, 50%, 100% 부하변동에 따른 각각의 Park's Vector Pattern을 비교하여 얻은 실험 결과는 제안한 방법의 유용성을 보여준다.

• 전기학회논문지
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• 제61권4호
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• pp.643-649
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• 2012

The diagnosis technique of stator winding faults based on Motor Current Signature Analysis(MCSA) was suggested. Park's vector pattern, the circle that is drawn by d-q transformed currents($i_d$, $i_q$), is widely used for stator winding faults detection. The current Distortion Ratio(DR), defined by the ratio of max axis and min axis of ellipse of Park's vector's pattern, was more simple and powerful method than the Park's vector pattern. In this study, a calculation method of distortion ratio of Park's vector pattern was suggested for auto diagnosis of stator winding short fault and usefulness of suggested calculation method of distortion ratio was verified through simulation using LabVIEW program.

• Journal of Power Electronics
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• 제17권4호
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• pp.972-982
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• 2017

The Park's vector of stator current is a popular technique for the detection of induction motor faults. While the detection of the faulty condition using the Park's vector technique is easy, the classification of different types of faults is intricate. This problem is overcome by the Multiple Park's Vector (MPV) approach proposed in this paper. In this technique, the characteristic fault frequency component (CFFC) of stator winding faults, rotor winding faults, unbalanced voltage and bearing faults are extracted from three phase stator currents. Due to constructional asymmetry, under the healthy condition these characteristic fault frequency components are unbalanced. In order to balanced them, a correction factor is added to the characteristic fault frequency components of three phase stator currents. Therefore, the Park's vector pattern under the healthy condition is circular in shape. This pattern is considered as a reference pattern under the healthy condition. According to the fault condition, the amplitude and phase of characteristic faults frequency components changes. Thus, the pattern of the Park's vector changes. By monitoring the variation in multiple Park's vector patterns, the type of fault and its severity level is identified. In the proposed technique, the diagnosis of faults is immune to the effects of unbalanced voltage and multiple faults. This technique is verified on a 7.5 hp three phase wound rotor induction motor (WRIM). The experimental analysis is verified by simulation results.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2011년도 제42회 하계학술대회
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• pp.2075-2076
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• 2011

The stator winding faults diagnosis technique based on MCSA is as follows. Firstly, collecting the 3 phase motor currents, that signal is transformed by (d-q transform, $i_d$, $i_q$). Park's vector pattern, the circle that is down by d-q transformed currents($i_d$, $i_q$). The circle is widely used for stator winding faults detection. The current distortion ratio(DR), defined by the ratio of max-axis and min-axis of ellipse of Park's vector's pattern. In this study, distortion ratio of Park's vector pattern is suggested for Auto diagnosis of stator winding short fault and usefulness of distortion ratio is verified through simulation using LabVIEW program.

• 전기학회논문지P
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• 제55권4호
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• pp.202-206
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• 2006

In this paper, an auto detection method of stator winding fault of small induction motor is suggested. The Park's vector pattern which is obtained from 3-phase current signal by d-q transforming, is very good to detect winding fault. Comparing the Park's vector pattern of testing motor with its of healthy motor, the Park's vector pattern of fault motor is became an ellipse and the asymmetry is increased by the winding fault series. So for detecting the dis-symmetry, id-filtered function, Min-value, and Max-value are suggested for auto detecting. Using LabVIEW programing, 3-phase healthy motor and several kind of winding fault motors are tested and the test results are shown that the suggested method can gives us a possibility of an auto detecting winding fault.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 D
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• pp.2070-2072
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• 2003

본 연구는 3상 소형 유도전동기의 고정자 권선 고장의 효과적인 진단을 위하여 고정자 전류에 대하여 Park's Vector를 이용한 기법을 적용하였다. 본 기법은 고정자 3상 전류를 측정하여 Park's vector 변환을 통하여 직축, 횡축 전류로 변환하고 이를 이용하여 고장 진단을 위한 Park's Vector Pattern을 통하여 고장진단을 수행하였다. 제안한 방법의 유용성을 확인하기 위하여 고정자 권선 한 상에 2턴, 10턴, 그리고 20턴의 단락고장을 발생시켜 정격부하의 25%, 50%, 100%에 대하여 부하변동에 따른 각각의 단락고장의 경우와 정상 전동기의 Park's Vector Pattern 비교하였으며 그 유용성을 확인하였다.

• 전기학회논문지P
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• 제57권2호
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• pp.160-163
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• 2008

In this paper, an auto-diagnosis method of the stator winding fault for small induction motor is suggested. 3-phase stator currents are sampled, filtered, and transformed with Park's vector transformation. After then Park's vector patterns are obtained. To detect the stator winding fault automatically, a distortion ratio is newly defined and compared with the one of healthy motor, and the threshold levels of distortion ratio are suggested. The 2-turn, 4-turn, 8-turn winding fault are tested with no load, 25%, 50%, 75%, and 100% rated load. The distortion ratio of the Park's vector patterns are increased as the increase of the faulted turns, but are same as the increase of the load.

• 전기전자학회논문지
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• 제24권3호
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• pp.883-889
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• 2020

본 논문에서는 CNN(Convolution Neural Network)을 이용한 유도전동기 고정자 고장진단에 PV(Park's Vector)패턴을 특징으로 활용하는 방법을 제안하였다. 기존의 CNN을 이용한 유도전동기 고장진단 방법은 3상 전류를 이미지화하여 진단을 수행하였으나, 이 방법은 인위적으로 전류의 시작점, 위상 등을 맞춰 정규화를 수행해야하는 번거러움이 존재하나, PV패턴을 이용할 경우 일정 원의 패턴을 나타내기 때문에 정규화의 문제를 해결 할 수 있었다. 또한 PV패턴을 이용할 경우, 특징벡터가 자동적으로 정규화됨에 따라 기존의 전류데이터를 이미지화한 결과보다 CNN의 정확도 측면에서 18.18[%] 우수함을 실험을 통해 확인할 수 있었다.

• Journal of information and communication convergence engineering
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• 제3권2호
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• pp.105-109
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• 2005

This paper proposed a fault diagnosis technique of induction motors winding fault based on an artificial neural network (ANN). This method used Park's vector pattern as input data of ANN. The ANN are firstly learned using this pattern, and then classify between 'healthy' and 'winding fault' (with 2, 10, and 20 shorted turn) induction motor under 0, 50, and $100\%$ load condition. Also the possibility of classification of untrained turn-fault and load condition are tested. The proposed method has been experimentally tested on a 3-phase, 1 HP squirrel-cage induction motor. The obtained results provided a high level of accuracy especially in small turn fault, and showed that it is a reliable method for industrial application

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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• pp.645-646
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• 2008

Silhouette extraction is widely used in many computer graphics applications. In this paper, we proposed a method for extracting 3D silhouette and internal pattern from 3D vector data. To do this, we first make an edge-list, secondly define the silhouette, and finally remove hidden lines. After getting the silhouette, we extract internal pattern using adjacent edge's dihedral. The proposed method not only effectively improves the performance of extracting 3D silhouette and internal pattern from 3D vector data but also reduces the computational complexity.