• Title, Summary, Keyword: Induction machine diagnosis

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Application of data fusion and Dempster-Skater theory in fault diagnosis of induction motors (데이터 융합과 Dempster-Shafer 이론을 이용한 유도전동기의 결함진단)

  • Kim, Kwang-Jin;Han, Tian;Yang, Bo-Suk
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • pp.549-555
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    • 2003
  • The technology of machine condition monitoring is used effectively to detect the machine faults at an early stage using different machine quantities, such as current, voltage, temperature and vibration. Induction motors are most widely used to drive pumps, compressors and fans in industrial drives. This paper presents approach to data fusion using Dempster-Shafer theory because only one technique has uncertainty. So we can obtain advanced accuracy of the machine fault diagnosis. Vibration and current quantities are applied to diagnose three-phase induction motor.

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Study of Rotor Asymmetry Effects of an Induction Machine by Finite Element Method

  • Abdesselam, Lebaroud;Guy, Clerc
    • Journal of Electrical Engineering and Technology
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    • v.6 no.3
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    • pp.342-349
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    • 2011
  • This paper presents a study on rotor asymmetry caused by broken bars and its effects on the stator current of an induction machine under an unbalanced supply voltage. The simulation of the induction machine is based on the finite element method. In the early stage of diagnosis, we show new sidebands specific to the partial rupture of the rotor bar. Experimental tests corroborate with the simulation results.

Rotor Failures Diagnosis of Squirrel Cage Induction Motors with Different Supplying Sources

  • Menacer, Arezki;Champenois, Gerard;Nait Said, Mohamed Said;Benakcha, Abdelhamid;Moreau, Sandrine;Hassaine, Said
    • Journal of Electrical Engineering and Technology
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    • v.4 no.2
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    • pp.219-228
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    • 2009
  • The growing application and the numerous qualities of induction motors (1M) in industrial processes that require high security and reliability levels has led to the development of multiple methods for early fault detection. However, various faults can occur, such as stator short-circuits and rotor failures. Traditionally the diagnosis machine is done through a sinusoidal power supply, in the present paper we study experimentally the effects of the rotor failures, such as broken rotor bars in function of the ac supplying, the load and show the impact of the converter from diagnosis of the machine. The technique diagnosis used is based on the spectral analysis of stator currents or stator voltages respectively according to the types of induction motor ac supplying. So, four different ac supplying are considered: ${\odot}$ the IM is directly by the balanced three-phase network voltage source, ${\odot}$ the IM is fed by a sinusoidal current source given the controlled by hysteresis, ${\odot}$ the IM is fed (in open loop) by a scalar control imposing through ratio V/f=constant, ${\odot}$ the IM is controlled through a vector control using space vector pulse width modulation (SVPWM) technique inverter with an outer speed loop.

Web-based Real Time Failure Diagnosis System Development for Induction Motor Bearing (유도전동기 베어링의 원거리 실시간 결함진단시스템 개발)

  • Kwon, Oh-Heon;Lee, Seung-Hyun
    • Journal of the Korean Society of Safety
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    • v.20 no.3
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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.

Development of Induction machine Diagnosis System using LabVIEW and PDA (LabVIEW 기반의 PDA를 이용한 기계 진단 시스템의 개발)

  • Son, Jong-Duk;Yang, Bo-Suk;Han, Tian;Ha, Jong-Yong
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • pp.945-948
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    • 2005
  • Mobile computing devices are becoming increasingly prevalent in a huge range of physical area, offering a considerable market opportunity. The focus of this paper is on the development of a platform of fault diagnosis system integrating with personal digital assistant (PDA). An improvement of induction machine rotor fault diagnosis based on AI algorithms approach is presented. This network system consists of two parts; condition monitoring and fault diagnosis by using Artificial Intelligence algorithm. LabVIEW allows easy interaction between acquisition instrumentation and operators. Also it can easily integrate AI algorithm. This paper presents a development environment fur intelligent application for PDA. The introduced configuration is a LabVIEW application in PDA module toolkit which is LabVIEW software.

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Classification of Induction Machine Faults using Time Frequency Representation and Particle Swarm Optimization

  • Medoued, A.;Lebaroud, A.;Laifa, A.;Sayad, D.
    • Journal of Electrical Engineering and Technology
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    • v.9 no.1
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    • pp.170-177
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    • 2014
  • This paper presents a new method of classification of the induction machine faults using Time Frequency Representation, Particle Swarm Optimization and artificial neural network. The essence of the feature extraction is to project from faulty machine to a low size signal time-frequency representation (TFR), which is deliberately designed for maximizing the separability between classes, a distinct TFR is designed for each class. The feature vectors size is optimized using Particle Swarm Optimization method (PSO). The classifier is designed using an artificial neural network. This method allows an accurate classification independently of load level. The introduction of the PSO in the classification procedure has given good results using the reduced size of the feature vectors obtained by the optimization process. These results are validated on a 5.5-kW induction motor test bench.

Intelligent Fault Diagnosis of Induction Motor Using Support Vector Machines (SVMs 을 이용한 유도전동기 지능 결항 진단)

  • Widodo, Achmad;Yang, Bo-Suk
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • pp.401-406
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    • 2006
  • This paper presents the fault diagnosis of induction motor based on support vector machine(SVMs). SVMs are well known as intelligent classifier with strong generalization ability. Application SVMs using kernel function is widely used for multi-class classification procedure. In this paper, the algorithm of SVMs will be combined with feature extraction and reduction using component analysis such as independent component analysis, principal component analysis and their kernel(KICA and KPCA). According to the result, component analysis is very useful to extract the useful features and to reduce the dimensionality of features so that the classification procedure in SVM can perform well. Moreover, this method is used to induction motor for faults detection based on vibration and current signals. The results show that this method can well classify and separate each condition of faults in induction motor based on experimental work.

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The Use of Support Vector Machines for Fault Diagnosis of Induction Motors

  • Widodo, Achmad;Yang, Bo-Suk
    • Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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    • pp.46-53
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    • 2006
  • This paper presents the fault diagnosis of induction motor based on support vector machine (SVMs). SVMs are well known as intelligent classifier with strong generalization ability. Application SVMs using kernel function is widely used for multi-class classification procedure. In this paper, the algorithm of SVMs will be combined with feature extraction and reduction using component analysis such as independent component analysis, principal component analysis and their kernel (KICA and KPCA). According to the result, component analysis is very useful to extract the useful features and to reduce the dimensionality of features so that the classification procedure in SVM can perform well. Moreover, this method is used to induction motor for faults detection based on vibration and current signals. The results show that this method can well classify and separate each condition of faults in induction motor based on experimental work.

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On the Detection of Induction-Motor Rotor Fault by the Combined “Time Synchronous Averaging-Discrete Wavelet Transform” Approach

  • Ngote, Nabil;Ouassaid, Mohammed;Guedira, Said;Cherkaoui, Mohamed
    • Journal of Electrical Engineering and Technology
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    • v.10 no.6
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    • pp.2315-2325
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    • 2015
  • Induction motors are widely used in industrial processes since they offer a very high degree of reliability. But like any other machine, they are vulnerable to faults, which if left unmonitored, might lead to an unexpected interruption at the industrial plant. Therefore, the condition monitoring of the induction motors have been a challenging topic for many electrical machine researchers. Indeed, the effectiveness of the fault diagnosis and prognosis techniques depends very much on the quality of the fault features selection. However, in induction-motor drives, rotor defects are the most complex in terms of detection since they interact with the supply frequency within a restricted band around this frequency, especially in the no-loaded case. To overcome this drawback, this paper deals with an efficient and new method to diagnose the induction-motor rotor fault based on the digital implementation of the monitoring algorithm based on the association of the Time Synchronous Averaging technique and Discrete Wavelet Transform. 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 combined “Time Synchronous Averaging – Discrete Wavelet Transform” approach.

On-line Faults Signature Monitoring Tool for Induction Motor Diagnosis

  • Medoued, Ammar;Lebaroud, Abdesselem;Boukadoum, Ahcene;Clerc, Guy
    • Journal of Electrical Engineering and Technology
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    • v.5 no.1
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    • pp.140-145
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    • 2010
  • The monitoring and the diagnosis of the faults in induction motors starting from the stator current are very interesting, since it is an accessible and measurable quantity. The spectral analysis of the stator current makes it possible to highlight the characteristic frequencies of the faults but in a wide frequency range depending on half the sampling frequency, making it very difficult to monitor on-line the faults. In order to facilitate the use of the relevant frequencies of machine faults we proposed the extraction of the frequency components using two methods, namely, the amplitude and the instantaneous frequency. The theoretical bases of these methods were presented and the results were validated on a test bench with an induction motor of 5.5 kw.