• Title/Summary/Keyword: rotor fault

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Fault Diagnosis Method based on Feature Residual Values for Industrial Rotor Machines

  • Kim, Donghwan;Kim, Younhwan;Jung, Joon-Ha;Sohn, Seokman
    • KEPCO Journal on Electric Power and Energy
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    • v.4 no.2
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    • pp.89-99
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    • 2018
  • Downtime and malfunction of industrial rotor machines represents a crucial cost burden and productivity loss. Fault diagnosis of this equipment has recently been carried out to detect their fault(s) and cause(s) by using fault classification methods. However, these methods are of limited use in detecting rotor faults because of their hypersensitivity to unexpected and different equipment conditions individually. These limitations tend to affect the accuracy of fault classification since fault-related features calculated from vibration signal are moved to other regions or changed. To improve the limited diagnosis accuracy of existing methods, we propose a new approach for fault diagnosis of rotor machines based on the model generated by supervised learning. Our work is based on feature residual values from vibration signals as fault indices. Our diagnostic model is a robust and flexible process that, once learned from historical data only one time, allows it to apply to different target systems without optimization of algorithms. The performance of the proposed method was evaluated by comparing its results with conventional methods for fault diagnosis of rotor machines. The experimental results show that the proposed method can be used to achieve better fault diagnosis, even when applied to systems with different normal-state signals, scales, and structures, without tuning or the use of a complementary algorithm. The effectiveness of the method was assessed by simulation using various rotor machine models.

Rotor Fault Detection System for Inverter Driven Induction Motors using Currents Signals and an Encoder

  • Kim, Nam-Hun
    • Journal of Power Electronics
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    • v.7 no.4
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    • pp.271-277
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    • 2007
  • In this paper, an induction motor rotor fault diagnosis system using current signals, which are measured using the axis-transformation method is presented. Inverter-fed motor drives, unlike line-driven motor drives, have stator currents which are rich in harmonics and therefore fault diagnosis using stator current is not trivial. The current signals for rotor fault diagnosis need precise and high resolution information, which means the diagnosis system demands additional hardware such as a low pass filter, high resolution ADC, an encoder and additional hardware. Therefore, the proposed axis-transformation method is expected to contribute to a low cost fault diagnosis system in inverter-fed motor drives without the need for any additional hardware. In order to confirm the validity of the developed algorithms, various experiments for rotor faults are tested and the line current spectrum of each faulty situation, using the Park transformation, is compared with the results obtained from the FFT(Fast Fourier Transform).

Low Cost Rotor Fault Detection System for Inverter Driven Induction Motor

  • Kim, Nam-Hun;Choi, Chang-Ho
    • Journal of Electrical Engineering and Technology
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    • v.2 no.4
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    • pp.500-504
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    • 2007
  • In this paper, the induction motor rotor fault diagnosis system using current signals, which are measured using axis-transformation method, and speed, which is estimated using current information, are presented. In inverter-fed motor drives unlike line-driven motor drives the stator currents have numerous harmonics components and therefore fault diagnosis using stator currents is very difficult. The current and speed signal for rotor fault diagnosis needs to be precise. Also, high resolution information, which means the diagnosis system, demands additional hardware such as low pass filter, high resolution ADC, encoder and etc. Therefore, the proposed axis-transformation and speed estimation method are expected to contribute to low cost fault diagnosis systems in inverter-fed motor drives without the need for an encoder and any additional hardware. In order to confirm validity of the developed algorithms, various experiments for rotor faults are tested and the line current spectrum of each faulty situation using Park transformation and speed estimation method are compared with the results obtained from fast Fourier transforms.

Monolith and Partition Schemes with LDA and Neural Networks as Detector Units for Induction Motor Broken Rotor Bar Fault Detection

  • Ayhan Bulent;Chow Mo-Yuen;Song Myung-Hyun
    • KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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    • v.5B no.2
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    • pp.103-110
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    • 2005
  • Broken rotor bars in induction motors can be detected by monitoring any abnormality of the spectrum amplitudes at certain frequencies in the motor current spectrum. Broken rotor bar fault detection schemes should rely on multiple signatures in order to overcome or reduce the effect of any misinterpretation of the signatures that are obscured by factors such as measurement noises and different load conditions. Multiple Discriminant Analysis (MDA) and Artificial Neural Networks (ANN) provide appropriate environments to develop such fault detection schemes because of their multi-input processing capabilities. This paper describes two fault detection schemes for broken rotor bar fault detection with multiple signature processing, and demonstrates that multiple signature processing is more efficient than single signature processing.

Feature Parameter Analysis for Rotor Fault Diagnosis (회전체 결함 진단을 위한 특징 파라미터 분석)

  • Jeoung, Rae-Hycuk;Chai, Jang-Bom;Lee, Byoung-Hak;Lee, Do-Hwan;Lee, Byung-Kon
    • The KSFM Journal of Fluid Machinery
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    • v.15 no.6
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    • pp.31-38
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    • 2012
  • Rotor of rotating machinery is the highly damaged part. Fault of 7 different types was confirmed as the main causes of rotor damage from the pump failure history data in domestic and U.S. nuclear. For each fault types, simulation testing was performed and fault signals were collected form the sensors. To calculate the statistical parameters of time-domain & frequency-domain, measured signals were analyzed by using the discrete wavelet transform, fast fourier transform, statistical analysis. Total 84 parameters were obtained. And Effectiveness factor were used to evaluate the discrimination capacity of each parameter. From the effectiveness factor, RAW-P4/RAW-P7/WT2-NNL/WT2-EE/WT1-P1 showed high ranking. Finally, these parameters were selected as the feature parameters of intelligent fault diagnostics for rotor.

The Development of Portable Rotor Bar Fault Diagnosis System for Three Phase Small Induction Motors Using LabVIEW (LaVIEW를 이용한 휴대용 3상 소형유도전동기 회전자 바 고장 진단 시스템 개발)

  • Song, Myung-Hyun;Park, Kyu-Nam;Han, Dong-Gi;Lee, Tae-Hun;Woo, Hyeok-Jae
    • The Transactions of the Korean Institute of Electrical Engineers P
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    • v.56 no.1
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    • pp.51-55
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    • 2007
  • In this paper, a portable rotor bar fault diagnosis system for small 3 phase induction motors is suggested. For portable real-tine diagnosis system, an USB-DAQ board for collecting the 3 phase current data, three current probes, and a notebook computer are used. The LabVIEW graphical language is used for filtering, analysis, storing, and monitoring the current data. The three phase stator current are filtered and transformed to frequency level by FIT. An analysis window programed by LabVIEW is located in front panel to show the FIT results and this suggested window has a zooming function to detect the fault feature more easily near the feature frequency range which is varying by the slip frequency. To show the possibility of portable rotor bar diagnosis system, three types(healthy, one rotor bar fault, two rotor bar fault) of rotor bar are intentionally prepared and compared by the suggested window of front panel. Experimental results are shown that a suggested diagnosis system is applicable to portable diagnosis system and the rotor bar fault is detected by the frequency window in front panel programed in LabVIEW graphical language.

Pattern Recognition of Rotor Fault Signal Using Bidden Markov Model (은닉 마르코프 모형을 이용한 회전체 결함신호의 패턴 인식)

  • Lee, Jong-Min;Kim, Seung-Jong;Hwang, Yo-Ha;Song, Chang-Seop
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.27 no.11
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    • pp.1864-1872
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    • 2003
  • Hidden Markov Model(HMM) has been widely used in speech recognition, however, its use in machine condition monitoring has been very limited despite its good potential. In this paper, HMM is used to recognize rotor fault pattern. First, we set up rotor kit under unbalance and oil whirl conditions. Time signals of two failure conditions were sampled and translated to auto power spectrums. Using filter bank, feature vectors were calculated from these auto power spectrums. Next, continuous HMM and discrete HMM were trained with scaled forward/backward variables and diagonal covariance matrix. Finally, each HMM was applied to all sampled data to prove fault recognition ability. It was found that HMM has good recognition ability despite of small number of training data set in rotor fault pattern recognition.

An Automatic Diagnosis for Rotor Bar Faults using Park's vector Pattern (팍스벡터 패턴을 이용한 회전자 바 고장 자동 진단)

  • Song, Myung-Hyun;Park, Kyu-Nam;Han, Dong-Gi;Yang, Chul-Oh
    • Proceedings of the KIEE Conference
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    • 2007.07a
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    • pp.361-363
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    • 2007
  • In this paper, an auto-diagnosis method of rotor bar fault for small induction motor is suggested. Usually FFT of stator currents are given the good results, but to detect the fault, slip is needed for calculating the feature frequency. The slip is varied as the load is changed. So in this paper, some alternative method for estimating the load is suggested. This method is based on the Park's vector pattern. The magnitudes of the feature frequency are compared with the threshhold that is predefined in the bounded range of load. The healthy rotor, single rotor bar fault and double rotor bar fault are tested with no load, 25%, 50%, 75%, and 100% rated load. From 50% to 100% rated load case, the rotor bar faults are detectable using indirect estimation of the load and the comparing the magnitudes of feature frequency. The no load case and under 40% rated load case, rotor fault are un detectable.

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Rotor Fault Detection System for the Inverter Driven Induction Motor using Current Signals

  • Kim, Nam-Hun;Baik, Won-Sik;Kim, Min-Huei;Choi, Chang-Ho
    • Journal of Power Electronics
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    • v.9 no.2
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    • pp.224-231
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    • 2009
  • The induction motor rotor fault diagnosis system using current signals, which are measured using an axis-transformation method, is presented in this paper. In inverter-fed motor drives, unlike line-driven motor drives, the stator currents are rich in harmonics; therefore fault diagnosis using stator current is not trivial. The current signals for rotor fault diagnosis need precise and high resolution information, which means the diagnosis system demands additional hardware such as a low pass filter, high resolution ADC, and encoder, etc. The proposed axis-transformation method with encoder and without encoder is expected to contribute to a low cost fault diagnosis system in inverter-fed motor drives without the need for any additional hardware. In order to confirm the validity of the developed algorithms, various experiments for rotor faults are tested and the line current spectrum of each faulty situation using Park transformation is compared with the results obtained from fast Fourier transforms.

Efficient Rotor Fault Detection of Induction Motors Using Stator Current Spectrum Monitoring (고정자 전류 스펙트럼 모니터링을 이용한 효과적인 유도전동기 회전자 고장 걸출)

  • 정춘호;우혁재;송명현;강의성;김경민
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.6 no.6
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    • pp.873-878
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    • 2002
  • Stator current spectrum by the fast Fourier transform (FFT) of current signals has been widely used for fault detection in induction motors. In this paper, we propose efficient rotor fault detection of Induction motors using stator current spectrum monitoring. The proposed method utilizes the mean absolute difference (MAD) between a Predetermined reference vector and a feature vector extracted from the stator current spectrum. Our proposed approach requires a smaller amount of computations when compared to fault detection algorithms based on neural networks, since it uses simple MAD criterion to detect rotor faults related broken rotor bars. Experimental results show that our proposed method can successively detect the rotor fault of the induction motor.