• 제목/요약/키워드: Induction motor fault

검색결과 196건 처리시간 0.029초

온라인 확률분포 추정기법을 이용한 확률모델 기반 유도전동기의 고장진단 시스템 (Stochastic Model based Fault Diagnosis System of Induction Motors using Online Probability Density Estimation)

  • 조현철;김광수;이권순
    • 전기학회논문지
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    • 제57권10호
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    • pp.1847-1853
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    • 2008
  • This paper presents stochastic methodology based fault detection algorithm for induction motor systems. We measure current of healthy induction motors by means of hall sensor systems and then establish its probability distribution. We propose online probability density estimation which is effective in real-time implementation due to its simplicity and low computational burden. In addition, we accomplish theoretical analysis to demonstrate convergence property of the proposed estimation by using statistical convergence and system stability theory. We apply our fault diagnosis approach to three-phase induction motors and achieve real-time experiment for evaluating its reliability and practicability in industrial fields.

확률분포추정기법을 이용한 유도전동기의 모델기반 고장진단 알고리즘 개발 (Model based Fault Detection and Diagnosis of Induction Motors using Probability Density Estimation)

  • 김광수;이영진;송헌혜;이권순
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2008년도 춘계학술대회 논문집 전기설비전문위원
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    • pp.171-173
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    • 2008
  • This paper presents stochastic methodology based fault diction and diagnosis algorithm for induction motor systems. First, we construct probability distribution model from healthy motors and then probability distribution for faulty motors is recursively calculated by means of the proposed probability estimation. We measure motor current with hall sensors as system state. The estimated probability is compared to the model to generate a residue signal which is utilized for fault detection and diagnosis, that is, where a fault is occurred. We carry out real-time induction motor experiment to evaluate efficiency and reliability of the proposed approach.

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온라인 확률추정기법을 이용한 모델기반 유도전동기의 고장진단 알고리즘 연구 (Model based Fault Detection and Diagnosis of Induction Motors using Online Probability Density Estimation)

  • 김광수;이영진;이권순
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2008년도 제39회 하계학술대회
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    • pp.1503-1504
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    • 2008
  • This paper presents stochastic methodology based fault diction and diagnosis algorithm for induction motor systems. First, we construct probability distribution model from healthy motors and then probability distribution for faulty motors is recursively calculated by means of the proposed probability estimation. We measure motor current with hall sensors as system state. The estimated probability is compared to the model to generate a residue signal which is utilized for fault detection and diagnosis, that is, where a fault is occurred. We carry out real-time induction motor experiment to evaluate efficiency and reliability of the proposed approach.

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인버터 구동 유도전동기의 계통사고 해석 (System Fault Analysis of Inverter Fed Induction Motor drives)

  • 권영목;김재철;송승엽;신중은
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2004년도 하계학술대회 논문집 A
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    • pp.304-306
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    • 2004
  • Recently, operating equipment with the high quality is necessary as technology is developing rapidly and equipment becomes more accurate. Therefore, the importance of diagnosis has been rising in modem industries. This paper presents that the induction motor is driven by invertor. We were using EMTP (Electromagnetic Transient Program) to study different characteristics of induction motor caused by faults; single phasing and the short circuit fault. After having the fault occurred in feeder cable, motor current, flux and torque waveform are analyzed

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전류신호 분석을 통한 유도전동기 고장진단시스템 연구 (A study on the fault diagnosis system for Induction motor using current signal analysis)

  • 변윤섭;장동욱;박현준;왕종배;이병송
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2001년도 춘계학술대회 논문집 전기기기 및 에너지변환시스템부문
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    • pp.19-21
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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.

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유도전동기 고장진단시스템 연구 (A study on the fault diagnosis system for Induction motor)

  • 변윤섭;박현준;김길동;한영재
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2001년도 하계학술대회 논문집 D
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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.

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The Fuzzy Fault Diagnosis System for Induction Motor

  • Sub, Byung-Yeun;Uk, Jang-Dong;Hyundai-Jun
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 2001년도 ICCAS
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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.

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LPC 분석 기법 및 EM 알고리즘 기반 잡음 환경에 강인한 진동 특징을 이용한 고 신뢰성 유도 전동기 다중 결함 분류 (High-Reliable Classification of Multiple Induction Motor Faults using Robust Vibration Signatures in Noisy Environments based on a LPC Analysis and an EM Algorithm)

  • 강명수;장원철;김종면
    • 한국컴퓨터정보학회논문지
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    • 제19권2호
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    • pp.21-30
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    • 2014
  • 최근 산업 현장에서 유도 전동기의 사용이 증대되고 있으며, 유도 전동기는 산업 현장에서 중요한 역할을 하고 있다. 따라서 유도 전동기의 결함으로 인한 피해를 최소화하기 위해 유도 전동기의 결함 검출 및 분류 시스템의 개발이 중요한 문제로 대두되고 있다. 따라서 본 논문에서는 유도전동기의 결함을 조기에 식별하기 위해 선형예측 코딩(LPC)기법과 Expectation Maximization(EM) 알고리즘을 이용하여 각각의 유도 전동기 고장의 스펙트럼 포락처리 모델을 추정한다. 앞서 두 기법을 사용하여 추정된 고장 유형 모델과 마할라노비스 거리(MD) 기법을 사용하여 유도전동기의 결합을 분류한다. 또한 제안된 알고리즘 성능을 평가하기 위해 기존에 제안된 진동 신호의 특징을 이용한 유도 전동기 결함 분류 알고리즘과 분류 정확도 측면에서 성능을 검증하였다. 실험 결과, 제안하는 알고리즘은 잡음이 없는 환경 및 잡음이 섞인 환경에서도 높은 분류 성능을 보였다.

Z-index와 주파수 분석을 이용한 유도전동기 고장진단과 분류 (Fault Detection and Classification of Faulty Induction Motors using Z-index and Frequency Analysis)

  • 이상혁
    • 한국안전학회지
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    • 제20권3호
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    • pp.64-70
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    • 2005
  • In this literature, fault detection and classification of faulty induction motors are carried out through Z-index and frequency analysis. Above frequency analysis refer Fourier transformation and Wavelet transformation. Z-index is defined as the similar form of energy function, also the faulty and healthy conditions are classified through Z-index. For the detection and classification feature extraction for the fault detection of an induction motor is carried out using the information from stator current. Fourier and Wavelet transforms are applied to detect the characteristics under the healthy and various faulty conditions. We can obtain feature vectors from two transformations, and the results illustrate that the feature vectors are complementary each other.