• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.645-648
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• 2003

Induction motors are a critical component of industrial processes. Sudden failures of such machines can cause the heavy economical losses and the deterioration of system reliability. Based on the reliability and cost competitiveness of driving system (motors), the faults detection and the diagnosis of system are considered very important factors. In order to perform the faults detection and diagnosis of motors, the vibration monitoring method and motor current signature analysis (MCSA) method are emphasized. In this paper, MCSA method is used for induction motor fault diagnosis. This method analyses the motor's supply current. since this diagnoses faults of the motor. The diagnostic algorithm is based on the principal component analysis(PCA), and the diagnosis system is programmed by using LabVIEW and MATLAB.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.10
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• pp.1882-1888
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• 2010

It is widely known that ESA(Electric Signature Analysis) method is very useful one for fault diagnosis of an induction motor. Online fault diagnosis system of induction motors using LabVIEW is proposed to detect the fault of broken rotor bars and shorted turns in stator. This system is not model-based system of induction motor but LabVIEW-based fault diagnosis system using FFT spectrum of stator current in faulty motor without estimating of motor parameters. FFT of stator current in faulty induction motor is measured and compared with various reference fault data in data base to diagnose the fault. This paper is focused on to predict and diagnose of the health state of induction motors in steady state. Also, it can be given to motor operator and maintenance team in order to enhance an availability and maintainability of induction motors. Experimental results are demonstrated that the proposed system is very useful to diagnose the fault and to implement the predictive maintenance of induction motors.

• Proceedings of the KSR Conference
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• 2003.10c
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• pp.529-534
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• 2003

Within industry induction motors have a broad application area to drive pumps, fans, elevators and electric trains. Sudden failures of such machines can cause the heavy economical losses and the deterioration of system reliability. Based on the reliability and cost competitiveness of driving system (motors), the faults detection and the diagnosis of system are considered very important factors. In order to perform the faults detection and diagnosis of motors, the vibration monitoring method and motor current signature analysis (MCSA) method are emphasized. In this paper, MCSA method are used for induction motor fault diagnosis. This method analyzes the motor's supply current, since this diagnoses faults of the motor. The diagnostic algorithm is based on the principal component analysis(PCA), and the diagnosis system is programmed by using LabVIEW and MATLAB.

• Journal of the Korean Society of Safety
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• v.20 no.3 s.71
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• pp.1-8
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• 2005

The industrial induction motor is widely used in the rotating electrical machine for the transmission of power. It is very reliable equipment, but it could lead to the loss of production and lift when failure occurs. Therefore, the failure data is acquired and analyzed by attaching an exclusive instrument to existing induction motor. However, these instruments could lead to side effects, increasing the production costs, because they are very expensive. The purpose of this study is the development of an induction motor bearing failure diagnosis system constructed using LabVIEW which can be supplied the kernelled function, process monitoring and current signature analysis. In addition, the availability and reasonability of the constructed system was examined for an induction motor with failure defects in outer raceway and ball bearing. From the results, it shows that failure diagnosis system constructed is useful for real-time monitoring with detection of bearing defects over the web.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.07a
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• pp.518-521
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• 2003

A new measurement system was developed by fault diagnosis system for traction motor using current signal analysis. The motor current signature analysis method is used for traction motor fault diagnosis. The diagnosis system program is constructed by artificial neural networks algorithm, those results from the program are used to train neural networks. The trained neural networks have the ability to compute adaptive results for non-trained inputs, and to calculate very fast due to original parallel structure of neural networks with high accuracy within destined tolerance. This system suggested that available test for checking, the probable extent of aging, and the rate of which aging is taking place.

• Proceedings of the KIPE Conference
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• 2001.10a
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• pp.365-368
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• 2001

The advantage of the squirrel cage induction motor is the brush less rotor. This advantage for operation and maintenance turns out to be a disadvantage for the detection of the cage rotor bar and endring defects, which means that the detection of cage faults is due to the measurement and analysis of only the stator input signals. The monitoring task in an inverter drive is complicated mainly because the voltage and current waveforms are nonsinusoidal and the high dv/dt values from fast switching inverterd distort the measurements. In this paper, we are going to discuss the detection method of broken rotor bar of the inverter fed squirrel cage induction motor by the motor current signature analysis(MCSA) and the opening terminal voltage signal analysis.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.559-568
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• 2014

Induction motors are critical components in industrial processes since their failure usually lead to an unexpected interruption at the industrial plant. The studies of induction motor behavior during abnormal conditions and the possibility to diagnose different types of faults have been a challenging topic for many electrical machine researchers. In this regard, an efficient and new method to detect the induction motor-fault may be the application of the Time Synchronous Averaging (TSA) to the stator current Park's Vector. The aim of this paper is to present a methodology by which defects in a three-phase wound rotor induction motor can be diagnosed. By exploiting the cyclostationarity characteristics of electrical signals, the TSA method is applied to the stator current Park's Vector, allowing the monitoring of the induction motor operation. Simulation and experimental results are presented in order to show the effectiveness of the proposed method. The obtained results are largely satisfactory, indicating a promising industrial application of the hybrid Park's Vector-TSA approach.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2172-2174
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• 2001

Induction motors are a critical component of many industrial machines and are frequently integrated in commercial equipment. The many economical losses and the deterioration of system reliability might be caused by the failure of induction motors in industrial field. Based on the reliability and cost competitiveness of driving system (motors), the faults detection and diagnosis of system is considered very important factors. In order to perform the faults detection and diagnosis of motors, the vibration monitoring method and motor current signature analysis (MCSA) method are emphasized. In this paper, MCSA method is used for induction motor fault diagnosis. This method analyzes the motor's supply current, since this diagnoses the motor's condition. The diagnostic system is constructed by using LabVIEW of National Instruments.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.65.1-65
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• 2001

Induction motors are a critical component of many industrial machines and are frequently integrated in commercial equipment. The many economical losses and the deterioration of system reliability might be caused by the failure of induction motors in industrial field. Based on the reliability and cost competitiveness of driving system motors, the faults detection and diagnosis of system is considered very important factors. In order to perform the faults detection and diagnosis of motors, the vibration monitoring method and motor current signature analysis MCSA method are emphasized. In this paper, MCSA method is used for induction motor fault diagnosis. This method analyzes the motor´s supply current, since this diagnoses the motor´s condition. The diagnostic system is constructed by using LabVIEW of National Instruments.

• Proceedings of the KIPE Conference
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• 2006.06a
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• pp.305-307
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• 2006

기존에 사용되어 온 진동데이터를 이용한 유도전동기 고장진단 기법은 유도전동기의 전기적 결함을 파악하기 어렵고 특정 고장의 경우 유사한 진동주파수를 포함하고 있어 정확한 고장진단이 어렵다. 본 논문에서는 유도전동기 고장진단 시스템을 구현하기 위해 기존의 진동데이터 분석에 전류 분석기법 중의 하나인 MCSA(Motor Current Signature Analysis)기법을 추가하여 유도전동기 예지보전시스템의 신뢰성을 향상시켰다. 구현된 시스템의 신뢰성을 검증하기 위해 유도전동기의 고장진단을 위한 실험환경을 구축하고 진동데이터만을 이용하여 얻어진 고장진단 결과와 전류데이터 분석을 병행하여 얻어진 고장진단 결과를 비교 분석하였다.