• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2304-2306
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• 2002

베어링 고장진단은 대부분 진동센서에 의한 근접 탐침에 의존하고 있어 설치 및 측정 상에 제약이 따른다. 최근 들어 전동기 전류를 이용한 베어링 고장진단의 가능성이 제시되고 있으나 베어링의 초기고장에 대한 연구는 없었다. 본 연구에서는 전동기 전류를 이용하여 베어링 외륜의 초기고장을 검출할 수 있는 기법을 제시하였다. 이 기법은 처리 데이터를 줄이고 신속한 고장검출을 위하여 고장진단 주파수 대역 설정방법을 제시하였으며 유도전동기 베어링 외륜 고장검출 실험을 통하여 이 기법의 유용성을 보였다.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.2069_2070
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• 2009

본 연구에서는 소형 유도전동기의 회전자 바 고장을 자동 진단을 함에 있어, 한 상 전류를 이용하여 센서 없이 회전자 바 고장을 진단하는 방법을 제안하였다. 먼저 한 상 전류를 이용하여 유도전동기의 회전 속도를 추정하고, 추정 된 유도전동기의 회전 속도를 이용해 회전자 바 고장의 특징 주파수를 계산하였다. 또 계산된 특징 주파수 대역의 주변의 일정 구간을 스캐닝하여 스캐닝 구간의 피크 값을 추출하는 방법으로 유도전동기 회전속도 추정의 오차를 보정함으로써 정확한 진단이 가능하게 하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.691-697
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• 2006

Induction motors are critical components in industrial process. So there are many research in the condition based maintenance, online monitoring system, and fault detection. This paper presents a scheme on the detection and diagnosis of the three-phase squirrel induction motor under unbalanced voltage, broken rotor bar, and a combination of these two faults. Actually one fault happen in operation, it influence other component in motor or cause another faults. Accordingly it is useful to diagnose and detect a combination fault in induction motor as well as each fault. The proposed fault detection and diagnosis algorithm is based on the stator currents from the squirrel induction motor and simulated with the aid of Matlab Simulink.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.1
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• pp.75-81
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• 2008

The induction motor is given a great deal of weight on the industry generally. Therefore, the fault of the induction motor may cause the fault to effect another parts or another faults in the whole system as well as in itself. These are accompany with a lose of the reliability in the industrial system. Accordingly to prevent these situation, the scholars have studies the fault diagnosis of the induction motor. In this paper, we proposed the diagnosis system of the induction motor. The method of diagnosis in proposed system is extracted the feature of the current signal by the wavelet transform. These extracted feature is used the automatic discrimination system by the neural network. We experiment the automatic discrimination system using the three faults imitation that often generated in the induction motor. The proposed system have achieved high reliable result with a simple devices about the three faults.

• Journal of the Korean Magnetics Society
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• v.6 no.5
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• pp.287-293
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• 1996

The squirrel cage rotors of induction motors may have several faults such as broken bars, bad spots in end ring and abnormal skew caused by improper processing. These faults may cause bad effects on the performance of the induction motor. This paper proposes the detecting technique of these faults by analyzing the induced current of the detecting electric magnet, using 2-D finite element method taking account of the rotor movement.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.819-825
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• 2007

In this paper, we propose a fault diagnosis algorithm of induction motors by DFT and wavelet. We extract a feature vector using a fault pattern extraction method by DFT in frequency domain and wavelet transform in time-frequency domain. And then we deal with a fusion algorithm for the feature vectors extracted from DFT and wavelet to classify the faults of induction motors. Finally, we provide an experimental results that the proposed algorithm can be successfully applied to classify the several fault signals acquired from induction motors.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.3
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• pp.75-84
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• 2005

Since an unexpected fault of induction motor drive systems can cause serious troubles in many industrial applications, which the technique is required to diagnose faults of a voltage-fed PWM inverter for induction motor drives. The considered fault types are rectifier diodes, switching devices and input terminals with open-circuit faults, and the signal for diagnosis is derived from motor currents. The magnitude of dq-current trajectory is used for the feature extraction of a fault and PCA LDA are applied to diagnose. Also, we show results with respect to the execution time because of the possibility to use that a diagnosis software is embedded in the controllers of medium and small size induction motors drive for real-time diagnosis. After we performed various simulations for the fault diagnosis of the inverter, the usefulness of proposed algerian was verified.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.7
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• pp.726-735
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• 2009

In this paper, we propose an approach to signal model-based fault detection and diagnosis system for induction motors. The current fault detection techniques used in the industry are limit checking techniques, which are simple but cannot predict the types of faults and the initiation of the faults. The system consists of two consecutive processes: fault detection process and fault diagnosis process. In the fault detection process, the system extracts the significant features from sound signals using combination of variance, cross-correlation and wavelet. Consequently, the pattern classification technique is applied to the fault diagnosis process to recognize the system faults based on faulty symptoms. The sounds generated from different kinds of typical motor's faults such as motor unbalance, bearing misalignment and bearing loose are examined. We propose two approaches for fault detection and diagnosis system that are waveletand-variance-based and wavelet-and-crosscorrelation-based approaches. The results of our experiment show more than 95 and 78 percent accuracy for fault classification, respectively.

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.1
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• pp.86-92
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• 2009

As increasing of using induction motors, the induction motors faults cause serious damage to the industry. Therefore to find out faults of induction motor is recognized as important problem awaiting solution. But to make matters worse, the faults of induction motors often progress through long time. It means that early diagnosis is very important. Many researches have been progressed and general method of diagnosis is using vibration sensor to diagnose fault of induction motor. However, although it is reliability technique, it demands high price and it is difficult to use. This paper presents an implementation of technique for fault diagnosis of induction motor using wavelet transform based stator current and it is composed with algorithm that decides whether fault existence or not using C++ based on windows software. The algorithm will be accomplished in real-time using current data acquisition board and PC automatically with Neural Network algorithm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.213-216
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• 2007

본 논문에서는 DFT(Discret Fourier Transform)과 웨이블렛을 이용한 고장진단 알고리즘을 제안한다. 제안된 방법은 주파수 기반의 DFT에 의한 고장패턴의 추출방법과 시간-주파수 기반의 웨이블렛을 이용한 고장패턴의 추출방법을 제안한다. 유도전동기의 진단을 DFT와 웨이블렛에 의해 추출된 특정값들을 효과적으로 융합할 수 있는 융합 알고리즘에 의해 수행된다. 개발된 알고리즘은 다양한 실측 데이터에 적용하여 그 타당성을 보이고자 한다.