Rotor Bar

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M.-h. Song - One of the best experts on this subject based on the ideXlab platform.

  • On the Use of a Lower Sampling Rate for Broken Rotor Bar Detection With DTFT and AR-Based Spectrum Methods
    IEEE Transactions on Industrial Electronics, 2008
    Co-Authors: Bulent Ayhan, Mo-yuen Chow, H.j. Trussell, M.-h. Song
    Abstract:

    Broken Rotor Bars in an induction motor create asymmetries and result in abnormal amplitude of the sidebands around the fundamental supply frequency and its harmonics. Motor current signature analysis (MCSA) techniques are applied to inspect the spectrum amplitudes at the broken Rotor Bar specific frequencies for abnormality and to decide about broken Rotor Bar fault detection and diagnosis. In this paper, we have demonstrated with experimental results that the use of a lower sampling rate with a digital notch filter is feasible for MCSA in broken Rotor Bar detection with discrete-time Fourier transform and autoregressive-based spectrum methods. The use of the lower sampling rate does not affect the performance of the fault detection, while requiring much less computation and low cost in implementation, which would make it easier to implement in embedded systems for motor condition monitoring.

  • multiple discriminant analysis and neural network based monolith and partition fault detection schemes for broken Rotor Bar in induction motors
    IEEE Transactions on Industrial Electronics, 2006
    Co-Authors: Bulent Ayhan, Mo-yuen Chow, M.-h. Song
    Abstract:

    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. It has been shown that these broken-Rotor-Bar specific frequencies are located around the fundamental stator current frequency and are termed lower and upper sideband components. 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 (ANNs) provide appropriate environments to develop such fault-detection schemes because of their multiinput-processing capabilities. This paper describes two fault-detection schemes for a broken-Rotor-Bar fault detection with a multiple signature processing and demonstrates that the multiple signature processing is more efficient than a single signature processing. The first scheme, which will be named the "monolith scheme," is based on a single large-scale MDA or ANN unit representing the complete operating load-torque region of the motor, while the second scheme, which will be named the "partition scheme," consists of many small-scale MDA or ANN units, each unit representing a particular load-torque operating region. Fault-detection performance comparison between the MDA and the ANN with respect to the two schemes is investigated using the experimental data collected for a healthy and a broken-Rotor-Bar case. Partition scheme distributes the computational load and complexity of the large-scale single units in a monolith scheme to many smaller units, which results in the increase of the broken-Rotor-Bar fault-detection performance, as is confirmed with the experimental results

  • Multiple signature processing-based fault detection schemes for broken Rotor Bar in induction motors
    IEEE Transactions on Energy Conversion, 2005
    Co-Authors: Bulent Ayhan, Mo-yuen Chow, M.-h. Song
    Abstract:

    The existence of 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. It has been shown that these broken Rotor Bar-specific frequencies are settled around the fundamental stator current frequency and are termed lower and upper sideband components. Broken Rotor Bar fault detection schemes should depend 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) provides an appropriate environment to develop such fault detection schemes because of its multi-input processing capabilities. The focus of this paper is to provide a new fault detection methodology for broken Rotor Bar fault detection and diagnostics in terms of its multiple signature processing feature and the motor operation partitioning concept to improve the overall detection performance. This paper describes two fault detection schemes within this methodology, and demonstrates that multiple signature processing is more efficient than single signature processing. The first scheme, which will be named the "monolith scheme," is based on a single large-scale MDA unit representing the complete operating load torque region of the motor, while the second scheme, which will be named the "partition scheme," consists of many small-scale MDA units, each unit representing a particular load torque operating region.

  • A case study on the comparison of non-parametric spectrum methods for broken Rotor Bar fault detection
    IECON'03. 29th Annual Conference of the IEEE Industrial Electronics Society (IEEE Cat. No.03CH37468), 2003
    Co-Authors: Bulent Ayhan, Mo-yuen Chow, H.j. Trussell, M.-h. Song
    Abstract:

    Broken Rotor Bars in an induction motor create asymmetries and result in abnormal amplitude of the sidebands around the fundamental supply frequency and its harmonics. Applying a spectrum analysis technique on motor current and inspecting the spectrum amplitudes at the broken Rotor Bar specific frequencies for abnormality is a well-known procedure for broken Rotor Bar fault detection and diagnosis. Among the spectrum analysis techniques for broken Rotor Bar fault detection, the fast Fourier transform (FFT) is the most widely used technique. There are other spectrum techniques, which are based on the power spectral density estimates. In this paper we compare the three well-known spectrum analysis methods: FFT, periodogram and Welch's periodogram methods according to their performance on the broken Rotor Bar fault detection problem. The results indicate that Welch's periodogram method has better fault discrimination capability and is more robust compared to the other two methods. A statistical hypothesis test applied to the results of the three methods depicts the comparison results quantitatively.

  • Mean absolute difference approach for induction motor broken Rotor Bar fault detection
    4th IEEE International Symposium on Diagnostics for Electric Machines Power Electronics and Drives 2003. SDEMPED 2003., 2003
    Co-Authors: M.-h. Song, E.-s. Kang, C.-h. Jeong, Mo-yuen Chow, Bulent Ayhan
    Abstract:

    This paper proposes the use of mean absolute difference (MAD) technique to perform broken Rotor Bar fault defection in induction motors. Feature extraction is performed on several motor current frequency components according to different load conditions in order to obtain meaningful feature vectors for broken Rotor Bar fault detection purposes. The MAD between the predetermined reference vector and the feature vector extracted from the input current spectrum is used to determine whether the motor has fault or not. The MAD approach is simple and can be effectively used for broken Rotor fault detection, provided that appropriate feature vector for fault information is used. Experimental results show that the proposed method effectively detects the broken Rotor Bar faults under different load conditions.

Bulent Ayhan - One of the best experts on this subject based on the ideXlab platform.

  • On the Use of a Lower Sampling Rate for Broken Rotor Bar Detection With DTFT and AR-Based Spectrum Methods
    IEEE Transactions on Industrial Electronics, 2008
    Co-Authors: Bulent Ayhan, Mo-yuen Chow, H.j. Trussell, M.-h. Song
    Abstract:

    Broken Rotor Bars in an induction motor create asymmetries and result in abnormal amplitude of the sidebands around the fundamental supply frequency and its harmonics. Motor current signature analysis (MCSA) techniques are applied to inspect the spectrum amplitudes at the broken Rotor Bar specific frequencies for abnormality and to decide about broken Rotor Bar fault detection and diagnosis. In this paper, we have demonstrated with experimental results that the use of a lower sampling rate with a digital notch filter is feasible for MCSA in broken Rotor Bar detection with discrete-time Fourier transform and autoregressive-based spectrum methods. The use of the lower sampling rate does not affect the performance of the fault detection, while requiring much less computation and low cost in implementation, which would make it easier to implement in embedded systems for motor condition monitoring.

  • multiple discriminant analysis and neural network based monolith and partition fault detection schemes for broken Rotor Bar in induction motors
    IEEE Transactions on Industrial Electronics, 2006
    Co-Authors: Bulent Ayhan, Mo-yuen Chow, M.-h. Song
    Abstract:

    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. It has been shown that these broken-Rotor-Bar specific frequencies are located around the fundamental stator current frequency and are termed lower and upper sideband components. 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 (ANNs) provide appropriate environments to develop such fault-detection schemes because of their multiinput-processing capabilities. This paper describes two fault-detection schemes for a broken-Rotor-Bar fault detection with a multiple signature processing and demonstrates that the multiple signature processing is more efficient than a single signature processing. The first scheme, which will be named the "monolith scheme," is based on a single large-scale MDA or ANN unit representing the complete operating load-torque region of the motor, while the second scheme, which will be named the "partition scheme," consists of many small-scale MDA or ANN units, each unit representing a particular load-torque operating region. Fault-detection performance comparison between the MDA and the ANN with respect to the two schemes is investigated using the experimental data collected for a healthy and a broken-Rotor-Bar case. Partition scheme distributes the computational load and complexity of the large-scale single units in a monolith scheme to many smaller units, which results in the increase of the broken-Rotor-Bar fault-detection performance, as is confirmed with the experimental results

  • Multiple signature processing-based fault detection schemes for broken Rotor Bar in induction motors
    IEEE Transactions on Energy Conversion, 2005
    Co-Authors: Bulent Ayhan, Mo-yuen Chow, M.-h. Song
    Abstract:

    The existence of 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. It has been shown that these broken Rotor Bar-specific frequencies are settled around the fundamental stator current frequency and are termed lower and upper sideband components. Broken Rotor Bar fault detection schemes should depend 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) provides an appropriate environment to develop such fault detection schemes because of its multi-input processing capabilities. The focus of this paper is to provide a new fault detection methodology for broken Rotor Bar fault detection and diagnostics in terms of its multiple signature processing feature and the motor operation partitioning concept to improve the overall detection performance. This paper describes two fault detection schemes within this methodology, and demonstrates that multiple signature processing is more efficient than single signature processing. The first scheme, which will be named the "monolith scheme," is based on a single large-scale MDA unit representing the complete operating load torque region of the motor, while the second scheme, which will be named the "partition scheme," consists of many small-scale MDA units, each unit representing a particular load torque operating region.

  • A case study on the comparison of non-parametric spectrum methods for broken Rotor Bar fault detection
    IECON'03. 29th Annual Conference of the IEEE Industrial Electronics Society (IEEE Cat. No.03CH37468), 2003
    Co-Authors: Bulent Ayhan, Mo-yuen Chow, H.j. Trussell, M.-h. Song
    Abstract:

    Broken Rotor Bars in an induction motor create asymmetries and result in abnormal amplitude of the sidebands around the fundamental supply frequency and its harmonics. Applying a spectrum analysis technique on motor current and inspecting the spectrum amplitudes at the broken Rotor Bar specific frequencies for abnormality is a well-known procedure for broken Rotor Bar fault detection and diagnosis. Among the spectrum analysis techniques for broken Rotor Bar fault detection, the fast Fourier transform (FFT) is the most widely used technique. There are other spectrum techniques, which are based on the power spectral density estimates. In this paper we compare the three well-known spectrum analysis methods: FFT, periodogram and Welch's periodogram methods according to their performance on the broken Rotor Bar fault detection problem. The results indicate that Welch's periodogram method has better fault discrimination capability and is more robust compared to the other two methods. A statistical hypothesis test applied to the results of the three methods depicts the comparison results quantitatively.

  • Mean absolute difference approach for induction motor broken Rotor Bar fault detection
    4th IEEE International Symposium on Diagnostics for Electric Machines Power Electronics and Drives 2003. SDEMPED 2003., 2003
    Co-Authors: M.-h. Song, E.-s. Kang, C.-h. Jeong, Mo-yuen Chow, Bulent Ayhan
    Abstract:

    This paper proposes the use of mean absolute difference (MAD) technique to perform broken Rotor Bar fault defection in induction motors. Feature extraction is performed on several motor current frequency components according to different load conditions in order to obtain meaningful feature vectors for broken Rotor Bar fault detection purposes. The MAD between the predetermined reference vector and the feature vector extracted from the input current spectrum is used to determine whether the motor has fault or not. The MAD approach is simple and can be effectively used for broken Rotor fault detection, provided that appropriate feature vector for fault information is used. Experimental results show that the proposed method effectively detects the broken Rotor Bar faults under different load conditions.

Mo-yuen Chow - One of the best experts on this subject based on the ideXlab platform.

  • On the Use of a Lower Sampling Rate for Broken Rotor Bar Detection With DTFT and AR-Based Spectrum Methods
    IEEE Transactions on Industrial Electronics, 2008
    Co-Authors: Bulent Ayhan, Mo-yuen Chow, H.j. Trussell, M.-h. Song
    Abstract:

    Broken Rotor Bars in an induction motor create asymmetries and result in abnormal amplitude of the sidebands around the fundamental supply frequency and its harmonics. Motor current signature analysis (MCSA) techniques are applied to inspect the spectrum amplitudes at the broken Rotor Bar specific frequencies for abnormality and to decide about broken Rotor Bar fault detection and diagnosis. In this paper, we have demonstrated with experimental results that the use of a lower sampling rate with a digital notch filter is feasible for MCSA in broken Rotor Bar detection with discrete-time Fourier transform and autoregressive-based spectrum methods. The use of the lower sampling rate does not affect the performance of the fault detection, while requiring much less computation and low cost in implementation, which would make it easier to implement in embedded systems for motor condition monitoring.

  • multiple discriminant analysis and neural network based monolith and partition fault detection schemes for broken Rotor Bar in induction motors
    IEEE Transactions on Industrial Electronics, 2006
    Co-Authors: Bulent Ayhan, Mo-yuen Chow, M.-h. Song
    Abstract:

    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. It has been shown that these broken-Rotor-Bar specific frequencies are located around the fundamental stator current frequency and are termed lower and upper sideband components. 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 (ANNs) provide appropriate environments to develop such fault-detection schemes because of their multiinput-processing capabilities. This paper describes two fault-detection schemes for a broken-Rotor-Bar fault detection with a multiple signature processing and demonstrates that the multiple signature processing is more efficient than a single signature processing. The first scheme, which will be named the "monolith scheme," is based on a single large-scale MDA or ANN unit representing the complete operating load-torque region of the motor, while the second scheme, which will be named the "partition scheme," consists of many small-scale MDA or ANN units, each unit representing a particular load-torque operating region. Fault-detection performance comparison between the MDA and the ANN with respect to the two schemes is investigated using the experimental data collected for a healthy and a broken-Rotor-Bar case. Partition scheme distributes the computational load and complexity of the large-scale single units in a monolith scheme to many smaller units, which results in the increase of the broken-Rotor-Bar fault-detection performance, as is confirmed with the experimental results

  • Multiple signature processing-based fault detection schemes for broken Rotor Bar in induction motors
    IEEE Transactions on Energy Conversion, 2005
    Co-Authors: Bulent Ayhan, Mo-yuen Chow, M.-h. Song
    Abstract:

    The existence of 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. It has been shown that these broken Rotor Bar-specific frequencies are settled around the fundamental stator current frequency and are termed lower and upper sideband components. Broken Rotor Bar fault detection schemes should depend 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) provides an appropriate environment to develop such fault detection schemes because of its multi-input processing capabilities. The focus of this paper is to provide a new fault detection methodology for broken Rotor Bar fault detection and diagnostics in terms of its multiple signature processing feature and the motor operation partitioning concept to improve the overall detection performance. This paper describes two fault detection schemes within this methodology, and demonstrates that multiple signature processing is more efficient than single signature processing. The first scheme, which will be named the "monolith scheme," is based on a single large-scale MDA unit representing the complete operating load torque region of the motor, while the second scheme, which will be named the "partition scheme," consists of many small-scale MDA units, each unit representing a particular load torque operating region.

  • A case study on the comparison of non-parametric spectrum methods for broken Rotor Bar fault detection
    IECON'03. 29th Annual Conference of the IEEE Industrial Electronics Society (IEEE Cat. No.03CH37468), 2003
    Co-Authors: Bulent Ayhan, Mo-yuen Chow, H.j. Trussell, M.-h. Song
    Abstract:

    Broken Rotor Bars in an induction motor create asymmetries and result in abnormal amplitude of the sidebands around the fundamental supply frequency and its harmonics. Applying a spectrum analysis technique on motor current and inspecting the spectrum amplitudes at the broken Rotor Bar specific frequencies for abnormality is a well-known procedure for broken Rotor Bar fault detection and diagnosis. Among the spectrum analysis techniques for broken Rotor Bar fault detection, the fast Fourier transform (FFT) is the most widely used technique. There are other spectrum techniques, which are based on the power spectral density estimates. In this paper we compare the three well-known spectrum analysis methods: FFT, periodogram and Welch's periodogram methods according to their performance on the broken Rotor Bar fault detection problem. The results indicate that Welch's periodogram method has better fault discrimination capability and is more robust compared to the other two methods. A statistical hypothesis test applied to the results of the three methods depicts the comparison results quantitatively.

  • Mean absolute difference approach for induction motor broken Rotor Bar fault detection
    4th IEEE International Symposium on Diagnostics for Electric Machines Power Electronics and Drives 2003. SDEMPED 2003., 2003
    Co-Authors: M.-h. Song, E.-s. Kang, C.-h. Jeong, Mo-yuen Chow, Bulent Ayhan
    Abstract:

    This paper proposes the use of mean absolute difference (MAD) technique to perform broken Rotor Bar fault defection in induction motors. Feature extraction is performed on several motor current frequency components according to different load conditions in order to obtain meaningful feature vectors for broken Rotor Bar fault detection purposes. The MAD between the predetermined reference vector and the feature vector extracted from the input current spectrum is used to determine whether the motor has fault or not. The MAD approach is simple and can be effectively used for broken Rotor fault detection, provided that appropriate feature vector for fault information is used. Experimental results show that the proposed method effectively detects the broken Rotor Bar faults under different load conditions.

M Rieraguasp - One of the best experts on this subject based on the ideXlab platform.

  • Rotor Bar breakage mechanism and prognosis in an induction motor
    IEEE Transactions on Industrial Electronics, 2015
    Co-Authors: Vicente Climentealarcon, Jose A Antoninodaviu, Elias G Strangas, M Rieraguasp
    Abstract:

    This paper proposes a condition-based maintenance and prognostics and health management (CBM/PHM) procedure for a Rotor Bar in an induction motor. The methodology is based on the results of a fatigue test intended to reproduce in the most natural way a Bar breakage in order to carry out a comparison between transient and stationary diagnosis methods for incipient fault detection. Newly developed techniques in stator-current transient analysis have allowed tracking the developing fault during the last part of the test, identifying the failure mechanism, and establishing a physical model of the process. This nonlinear failure model is integrated in a particle filtering algorithm to diagnose the defect at an early stage and predict the remaining useful life of the Bar. An initial generalization of the results to conditions differing from the ones under which the fatigue test was developed is studied.

  • an emd based invariant feature extraction algorithm for Rotor Bar condition monitoring
    IEEE International Symposium on Diagnostics for Electric Machines Power Electronics and Drives, 2011
    Co-Authors: Jose A Antoninodaviu, Elias G Strangas, M Rieraguasp, J Rogerfolch, Selin Aviyente, Rafael B Perez
    Abstract:

    Development of portable devices for reliable condition monitoring of induction machines has become the goal of many researchers. In this context, the development of robust algorithms for the automatic diagnosis of electromechanical failures plays a crucial role. The conventional tool for the diagnostic of most faults is based on the FFT of the steady-state current. However, it implies significant drawbacks in industrial applications in which the machine does not operate under ideal stationary conditions (e.g. presence of pulsating load torques, supply unbalances, noises…). In order to overcome some of these problems, a novel transient-based methodology (Transient Motor Current Signature Analysis, TMCSA) has been recently proposed. The idea is to analyze the current demanded by the machine under transient operation (e.g. during the startup) by using proper Time Frequency Decomposition (TFD) tools in order to identify the presence of specific patterns in the time-frequency map caused by the characteristic evolutions of fault-related components. However, despite the excellent results hitherto obtained, the qualitative identification of the patterns requires a certain user expertness, which implies difficulties for the automation of the diagnosis. A new algorithm for the automatic diagnostic of Rotor Bar failures is proposed in this paper. It is based on the application of the Hilbert-Huang Transform, sustained on the Empirical Mode Decomposition process, for feature extraction, and the further application of the Scale Transform (ST) for invariant feature selection. The results prove the reliability of the algorithm and its generality to automatically diagnose the fault in machines with rather different sizes and load conditions.

  • a scale invariant algorithm for the automatic diagnosis of Rotor Bar failures in induction motors
    International Symposium on Industrial Electronics, 2011
    Co-Authors: Jose A Antoninodaviu, Elias G Strangas, Selin Aviyente, M Rieraguasp
    Abstract:

    Development of reliable algorithms for the automatic diagnosis of broken Rotor Bars in induction motors (IM) has become the concern of many researchers during these previous decades. Though conventional steady-state current-based diagnosis approaches have behaved well for certain industrial applications, they may be not suitable in cases in which the machine does not operate under ideal stationary conditions (e.g. presence of load torque oscillations, supply unbalances, noises…). Due to this fact, alternative transient-based techniques based on the application of Time-frequency Decomposition (TFD) tools, have been introduced. They have shown satisfactory results, even in cases in which the conventional methodology does not work properly. Nonetheless, necessity of user expertness for the qualitative interpretation of the resulting time-frequency fault-related patterns as well as lack of automation in the diagnosis process makes often difficult their potential implementation in portable condition monitoring devices. A new algorithm for the automatic diagnostic of Rotor Bar failures is proposed in this paper. It takes as a basis the wavelet signals resulting from the Discrete Wavelet Transform (DWT) of the startup current, which contain basic fault-related features. These signals are further processed to generate 2-D images containing characteristic L-shaped patterns associated with the analyzed fault. Subsequent application of the scale transform allows obtaining scale-invariant feature matrices. Final correlation between these matrices enables to diagnose the condition of the machine. Test results prove the reliability of the algorithm and its generality to automatically diagnose the fault in machines with rather different sizes and load conditions.

  • the use of the wavelet approximation signal as a tool for the diagnosis of Rotor Bar failures
    IEEE Transactions on Industry Applications, 2008
    Co-Authors: M Rieraguasp, Jose A Antoninodaviu, J Rogerfolch, M Molina P Palomares
    Abstract:

    The aim of this paper is to present a new approach for Rotor Bar failure diagnosis in induction machines. The method focuses on the study of an approximation signal resulting from the wavelet decomposition of the startup stator current. The presence of the left sideband harmonic is used as evidence of the Rotor failure in most diagnosis methods based on the analysis of the stator current. Thus, a detailed description of the evolution of the left sideband harmonic during the startup transient is given in this paper; for this purpose, a method for calculating the evolution of the left sideband during the startup is developed, and its results are physically explained. This paper also shows that the approximation signal of a particular level, which is obtained from the discrete wavelet transform of the startup stator current, practically reproduces the time evolution of the left sideband harmonic during the startup. The diagnosis method proposed here consists of checking if the selected approximation signal fits well the characteristic shape of the left sideband harmonic evolution described in this paper. The method is validated through laboratory tests. The results prove that it can constitute a useful tool for the diagnosis of Rotor Bar breakages.

Tayfun Gundogdu - One of the best experts on this subject based on the ideXlab platform.

  • influence of stator and Rotor geometric parameters on Rotor Bar current waveform and performance of ims
    The Journal of Engineering, 2019
    Co-Authors: Tayfun Gundogdu, Z. Q. Zhu, J. C. Mipo, Sophie Personnaz
    Abstract:

    In this study, the influence of the stator and Rotor geometric parameters on the Rotor Bar current waveform and performance characteristics of a squirrel-cage induction machine (IM), based on a slot/pole number combination (48-stator slot/8-pole/52-Rotor slot), with the same winding layout, stator outer diameter, stack length, stator current, and rated speed as the Toyota Prius 2010 interior permanent magnet machine, is investigated. In order to reveal the individual influence of each parameter, parametric analyses have been conducted for each geometric parameter, and the corresponding variations of torque, torque ripple, power losses, and efficiency are numerically studied. It has been shown that in addition to Rotor slot width, Rotor slot depth, and stator slot width, corresponding to Rotor tooth width, Rotor tooth height, and stator tooth width, respectively, have a significant effect on the Bar current waveform and performance characteristics while the slot opening widths have the least effect.

  • influence of stator slot and pole number combination on Rotor Bar current waveform and performance of induction machines
    International Conference on Electrical Machines and Systems, 2017
    Co-Authors: Tayfun Gundogdu, Z. Q. Zhu, J. C. Mipo
    Abstract:

    This paper investigates the influence of stator slot/pole number combinations on the Rotor Bar current waveform and electromagnetic performance of a squirrel-cage induction machines (IMs) designed with a slot number per pole per phase of two. 36-slot/6-pole (36S6P), 48S8P, and 60S10P IMs, all having 52 Rotor slots, have been designed by using the same winding layout, stator outer diameter, stack length, stator current, and rated speed as the Toyota Prius 2010 interior permanent magnet machine (IPM). The waveforms of flux density in different parts of stator and Rotor, and the leakage flux in the slots are considered and their influences are investigated. It has been revealed that even if the winding layout is the same, the stator slot/pole number combination has a considerable effect on the distortion level of the Bar current and the electromagnetic performance, including torque, torque ripple, power factor, and efficiency.

  • Influence of air-gap length on Rotor Bar current waveform of squirrel-cage induction motor
    2016
    Co-Authors: Tayfun Gundogdu, Z. Q. Zhu, J. C. Mipo, P. Farah
    Abstract:

    The Rotor Bar current in an induction motor (IM) is usually assumed to be sinusoidal. However, it is found that the Rotor Bar current may become non-sinusoidal even if the stator windings are fed with a sinusoidal source. Influence of air-gap length on the Rotor Bar current waveform of an IM, which is designed by using the same geometrical and operational parameters as the Toyota Prius 2010 interior permanent magnet machine, has been investigated in depth. It reveals that the air-gap length has a significant effect on the Rotor Bar current waveform, torque ripple, slot leakage, and efficiency. The conditions when the non-sinusoidal Rotor Bar current waveform occurs and the reasons behind this phenomenon are investigated by finite element method (FEM).

  • Investigation of non-sinusoidal Rotor Bar current phenomenon in induction machines — Influence of slip and electric loading
    2016 XXII International Conference on Electrical Machines (ICEM), 2016
    Co-Authors: Tayfun Gundogdu, Z. Q. Zhu, J. C. Mipo, P. Farah
    Abstract:

    Rotor Bar current of an induction machine (IM) designed by using the same stator outer diameter, stack length, air-gap length, output power and rated speed as the Toyota Prius 2010 interior permanent magnet machine is investigated. It is observed that in some cases the Rotor Bar current waveform can be non-sinusoidal even if the stator windings are fed with a sinusoidal source. The conditions for such non-sinusoidal Rotor Bar current to occur and the reasons behind this phenomenon are investigated by finite element method, with particular reference to the influence of slip and electric loading.

  • investigation of non sinusoidal Rotor Bar current phenomenon in induction machines influence of slip and electric loading
    International Conference on Electrical Machines, 2016
    Co-Authors: Tayfun Gundogdu, Z. Q. Zhu, J. C. Mipo, P. Farah
    Abstract:

    Rotor Bar current of an induction machine (IM) designed by using the same stator outer diameter, stack length, air-gap length, output power and rated speed as the Toyota Prius 2010 interior permanent magnet machine is investigated. It is observed that in some cases the Rotor Bar current waveform can be non-sinusoidal even if the stator windings are fed with a sinusoidal source. The conditions for such non-sinusoidal Rotor Bar current to occur and the reasons behind this phenomenon are investigated by finite element method, with particular reference to the influence of slip and electric loading.

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