• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.6
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• pp.55-62
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• 2006

For the fault diagnosis of three-phase induction motors, we construct a experimental unit and then develop a diagnosis algorithm based on pattern recognition. The experimental unit consists of machinery module for induction motor drive and data acquisition module to obtain the fault signal. As the first step for diagnosis procedure, preprocessing is performed to make the acquired current simplified and normalized. To simplify the data, three-phase current is transformed into the magnitude of Concordia vector. As the next step, feature extraction is performed by kernel principal component analysis(KPCA) and linear discriminant analysis(LDA). Finally, we used the classifier based on radial basis function(RBF) network. To show the effectiveness, the proposed diagnostic system has been intensively tested with the various data acquired under different electrical and mechanical faults with varying load.

• Journal of IKEEE
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• v.24 no.3
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• pp.883-889
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• 2020

In this paper, we propose a method to use PV(Park's Vector) pattern for inductive motor stator fault diagnosis using CNN(Convolution Neural Network). The conventional CNN based fault diagnosis method was performed by imaging three-phase currents, but this method was troublesome to perform normalization by artificially setting the starting point and phase of current. However, when using PV pattern, the problem of normalization could be solved because the 3-phase current shows a certain circular pattern. In addition, the proposed method is proved to be superior in the accuracy of CNN by 18.18[%] compared to the previous current data image due to the autonomic normalization.

• KIPE Magazine
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• v.15 no.4
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• pp.38-41
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• 2010

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.6
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• pp.1291-1296
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• 2003

This paper deals with efficient diagnostic for stator winding fault of 3-phase induction motor using a current Park's vector approach. This method firstly transforms 3-phase stator current to vertical axis current and horizontal axis current of Park's Vector, and then obtains the each Park's Vector Pattern and detects stator winding fault by comparing to Park's Vector Pattern of healthy and fault. Experimental results, obtained by using induction motor having inter-turn fault of 2, 10, 20 turn, demonstrate the effectiveness of the proposed technique, for detecting the presence of stator winding fault under 25%, 50%, and 100% of full load condition.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2031-2033
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• 2003

본 논문에서는 25%, 50%, 75%, 100% 정격 부하 아래에서 b유도전동기의 회전자 고장을 검출하기 위한 효과적인 FFT 기반 알고리즘을 제안하였다. 제안한 방법은 고정자 전류 스펙트럼 성분 중에서 회전자 고장에 큰 영향을 주는 주파수 성분에서 미리 결정한 기준벡터와 특정벡터 사이의 평균 절대치 차이(Mean Absolute Difference)를 이용하였다. 기준벡터는 정상 상태의 고정자 전류 스펙트럼 성분 중에서 기본 주파수 상, 하의 두개의 측파대 주변의 좁은 영역에서 추출하였고 특징벡터는 정상상태와 회전자 바 고장상태의 고정자 전류 스펙트럼 성분 중에서 또한 기준벡터와 동일한 영역에서 추출하였다. 부하실험을 통하여 제안한 알고리즘의 적용 결과는 각각의 정격 부하에서 유도전동기의 회전자 바 고장을 효과적으로 검출할 수 있음을 입증하였다.

• Proceedings of the KIPE Conference
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• 2014.07a
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• pp.518-519
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• 2014

선형 유도 전동기 구동 인버터의 스위치 결함에 대한 신뢰성 향상을 위해, 실제 온라인 기반의 고장 및 고장점 검출에 대한 연구가 활발하다. 인버터에서 스위치 고장이 발생하면, 일반적으로 전압, 전류에 고조파가 발생되고 토크의 리플이 증가하게 된다. 또한, 인버터 스위치 고장의 경우 과전류가 발생하며 다른 전자 부품에 2차 피해를 일으킬 수 있다. 본 논문에서는 센서를 통해 얻은 전류의 정보를 이용하여 고장 및 고장 스위치를 검출하는 알고리즘을 제안한다. 한 개 또는 두 개의 스위치 고장을 4개의 faulty group으로 구별하여 고장 검출이 이루어지며 제안된 알고리즘을 입증하기 위해 Matlab-Simulink을 이용한 시뮬레이션 결과와 실험 결과가 제시된다.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.878-882
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• 2004

In this paper, we propose a hierarchical hybrid neural network for detecting faults of induction motor. Implementing the classifier based on the input and output data, we apply appropriate transform and classification method at each step. In the proposed method, after obtaining the current of state of motor for each period, we transform it by Principle Component Analysis(PCA) to reduce its dimension. Before the training process, we use the conditional Fuzzy C-means(FCM) for obtaining the initial parameters of neural network for more effective learning procedure. From the various simulations, we find that the proposed method shows better performance to detect and diagnosis of induction motor and compare than other methods.

• Journal of Advanced Marine Engineering and Technology
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• v.31 no.1
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• pp.95-102
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• 2007

The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis and prognosis are of increasing importance. This study introduces a technique to detect and identify faults in induction motors. Stator currents were measured and stored by time domain. The time domain is not suitable for representing current signals, so wavelet transform is used to convert the signals onto frequency domain. The raw signals can not show the significant feature, therefore difference values between the signal of the health conditions and that of the fault conditions are applied. The difference values were transformed by wavelet transform and the features are extracted from the transformed signals. The dynamic time warping method was used to identify the fault type. This study describes the results of detecting fault using wavelet analysis.

• Plant Journal
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• v.11 no.2
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• pp.39-44
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• 2015

According to a research that is based on a previous study, But in a different way, This study shows fault diagnosis of Induction motor bearing which runs in coal-fired power plant industries on Desulfurization absorber agitator using Spectrum analysis of Stator Current and visual inspection. As a result of harmonic content analysis of stator current spectrum, It was possible to detect ball and outer race fault frequency. The comparison in the context of this experiment proves that the amplitude of faulty frequency is increased in three times at a fault in ball and in outer race. Spectrum analysis of stator current can be used to detect the presence of a fault condition as well as experiment in faulty bearings, besides early fault detection in bearings can prevent unexpected power generation loss and emergency maintenance cost.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1063-1064
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• 2007

본 연구는 유도전동기 진단시스템을 개발하기 위하여 테스트 전동기 내부에 취부된 자속센서 신호를 사용한 알고리즘 적용 결과를 논한 것으로서 분류기별 고장 판별 정확도에 대하여 서술하였다. 특징추출은 Kernel Principal Component Analysis (KPCA) 방법을 이용 하였으며, 테스트 샘플들에 대해서는 LDA(Linear Discriminant Analysis)와 k-NN(k-Nearest neighbors) 분류기법을 이용하여 판별하였다. 회전자 바 손상이나 편심(동적/정적)인 경우는 두 가지 분류기 모두 95[%]이상의 높은 분류 정확도를 보였지만, LDA인 경우 정상상태를 비롯한 베이링 불량이나, 샤프트 변형인 경우는 낮은 분류율을 보였다.