The Experts below are selected from a list of 74907 Experts worldwide ranked by ideXlab platform
Tie Wang - One of the best experts on this subject based on the ideXlab platform.
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a new method of accurate broken rotor bar diagnosis based on modulation signal bispectrum analysis of motor current signals
2015Co-Authors: Tie Wang, Ahmed Alwodai, Xiange Tian, Yimin Shao, Andrew BallAbstract:Abstract Motor current signature analysis (MCSA) has been an effective way of monitoring electrical machines for many years. However, inadequate accuracy in diagnosing incipient broken rotor bars (BRB) has motivated many studies into improving this method. In this paper a modulation signal bispectrum (MSB) analysis is applied to motor currents from different broken bar cases and a new MSB based sideband estimator (MSB-SE) and sideband amplitude estimator are introduced for obtaining the amplitude at ( 1 ± 2 s ) f s (s is the rotor slip and f s is the fundamental Supply Frequency) with high accuracy. As the MSB-SE has a good performance of noise suppression, the new estimator produces more accurate results in predicting the number of BRB, compared with conventional power spectrum analysis. Moreover, the paper has also developed an improved model for motor current signals under rotor fault conditions and an effective method to decouple the BRB current which interferes with that of speed oscillations associated with BRB. These provide theoretical supports for the new estimators and clarify the issues in using conventional bispectrum analysis.
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fault diagnosis of motor drives using stator current signal analysis based on dynamic time warping
2013Co-Authors: Dong Zhe, Tie Wang, Andrew AllAbstract:Electrical motor stator current signals have been widely used to monitor the condition of induction machines and their downstream mechanical equipment. The key technique used for current signal analysis is based on Fourier transform (FT) to extract weak fault sideband components from signals predominated with Supply Frequency component and its higher order harmonics. However, the FT based method has limitations such as spectral leakage and aliasing, leading to significant errors in estimating the sideband components. Therefore, this paper presents the use of dynamic time warping (DTW) to process the motor current signals for detecting and quantifying common faults in a downstream two-stage reciprocating compressor. DTW is a time domain based method and its algorithm is simple and easy to be embedded into real-time devices. In this study DTW is used to suppress the Supply Frequency component and highlight the sideband components based on the introduction of a reference signal which has the same Frequency component as that of the Supply power. Moreover, a sliding window is designed to process the raw signal using DTW frame by frame for effective calculation. Based on the proposed method, the stator current signals measured from the compressor induced with different common faults and under different loads are analysed for fault diagnosis. Results show that DTW based on residual signal analysis through the introduction of a reference signal allows the Supply components to be suppressed well so that the fault related sideband components are highlighted for obtaining accurate fault detection and diagnosis results. In particular, the root mean square (RMS) values of the residual signal can indicate the differences between the healthy case and different faults under varying discharge pressures. It provides an effective and easy approach to the analysis of motor current signals for better fault diagnosis of the downstream mechanical equipment of motor drives in the time domain in comparison with conventional FT based methods.
Andrew Ball - One of the best experts on this subject based on the ideXlab platform.
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a new method of accurate broken rotor bar diagnosis based on modulation signal bispectrum analysis of motor current signals
2015Co-Authors: Tie Wang, Ahmed Alwodai, Xiange Tian, Yimin Shao, Andrew BallAbstract:Abstract Motor current signature analysis (MCSA) has been an effective way of monitoring electrical machines for many years. However, inadequate accuracy in diagnosing incipient broken rotor bars (BRB) has motivated many studies into improving this method. In this paper a modulation signal bispectrum (MSB) analysis is applied to motor currents from different broken bar cases and a new MSB based sideband estimator (MSB-SE) and sideband amplitude estimator are introduced for obtaining the amplitude at ( 1 ± 2 s ) f s (s is the rotor slip and f s is the fundamental Supply Frequency) with high accuracy. As the MSB-SE has a good performance of noise suppression, the new estimator produces more accurate results in predicting the number of BRB, compared with conventional power spectrum analysis. Moreover, the paper has also developed an improved model for motor current signals under rotor fault conditions and an effective method to decouple the BRB current which interferes with that of speed oscillations associated with BRB. These provide theoretical supports for the new estimators and clarify the issues in using conventional bispectrum analysis.
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early fault detection using a novel spectrum enhancement method for motor current signature analysis
2008Co-Authors: Yue Zhang, Guojun Qin, Andrew BallAbstract:This paper develops a novel spectrum enhancement method to simplify the spectrum representation. It uses a forward synchronous transform to convert three phase current signals into the two current signals in the synchronous reference frame. The spectrum obtained by applying Fast Fourier Transform (FFT) to one of the transformed current signals is then maximized by varying the initial phase in the synchronous transform. The enhanced spectrum is a much concise representation of the fault signal because it not only eliminates the Supply Frequency but also enlarges the magnitudes of fault components. The capabilities in enhancing the fault signals and controlling the noise are highlighted through both theoretical analysis and numerical simulation. In addition, two common motor fault cases: a broken rotor bar and a small rotor eccentricity are also tested to evaluate the performance of the enhanced spectrum in detecting these incipient motor faults.
Andrew All - One of the best experts on this subject based on the ideXlab platform.
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fault diagnosis of motor drives using stator current signal analysis based on dynamic time warping
2013Co-Authors: Dong Zhe, Tie Wang, Andrew AllAbstract:Electrical motor stator current signals have been widely used to monitor the condition of induction machines and their downstream mechanical equipment. The key technique used for current signal analysis is based on Fourier transform (FT) to extract weak fault sideband components from signals predominated with Supply Frequency component and its higher order harmonics. However, the FT based method has limitations such as spectral leakage and aliasing, leading to significant errors in estimating the sideband components. Therefore, this paper presents the use of dynamic time warping (DTW) to process the motor current signals for detecting and quantifying common faults in a downstream two-stage reciprocating compressor. DTW is a time domain based method and its algorithm is simple and easy to be embedded into real-time devices. In this study DTW is used to suppress the Supply Frequency component and highlight the sideband components based on the introduction of a reference signal which has the same Frequency component as that of the Supply power. Moreover, a sliding window is designed to process the raw signal using DTW frame by frame for effective calculation. Based on the proposed method, the stator current signals measured from the compressor induced with different common faults and under different loads are analysed for fault diagnosis. Results show that DTW based on residual signal analysis through the introduction of a reference signal allows the Supply components to be suppressed well so that the fault related sideband components are highlighted for obtaining accurate fault detection and diagnosis results. In particular, the root mean square (RMS) values of the residual signal can indicate the differences between the healthy case and different faults under varying discharge pressures. It provides an effective and easy approach to the analysis of motor current signals for better fault diagnosis of the downstream mechanical equipment of motor drives in the time domain in comparison with conventional FT based methods.
Vinod Khadkikar - One of the best experts on this subject based on the ideXlab platform.
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Artificial-Neural-Network-Based Phase-Locking Scheme for Active Power Filters
2014Co-Authors: Mohammed Qasim, Parag Kanjiya, Vinod KhadkikarAbstract:This paper presents a phase-locking control scheme based on artificial neural networks (ANNs) for active power filters (APFs). The proposed phase locking is achieved by estimating the fundamental Supply Frequency and by generating a phase-locking signal. The nonlinear-least-squares-based approach is modified to estimate the Supply Frequency. To improve the accuracy of Frequency estimation, when the Supply voltage contains harmonics that are not known, a prefiltering stage is introduced. In shunt APF applications, not only the information of Frequency is sufficient but also the phase information of the Supply voltage is required to generate a unit template that is phase-locked to the Supply voltage. Therefore, in this paper, an adaptive-linear-neuron-based scheme is proposed to extract the phase information of the Supply voltage. The estimated system Frequency and phase information are then utilized to generate a phase-locking signal that assures a perfect synchronization with the fundamental Supply voltage. To demonstrate the effectiveness of the proposed approach, the synchronous reference frame ( d-q theory) shunt APF control method with the proposed ANN-based phase-locking scheme is adopted. The performance of the proposed ANN-based approach is verified experimentally with different Supply systems and load conditions.
Dong Zhe - One of the best experts on this subject based on the ideXlab platform.
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fault diagnosis of motor drives using stator current signal analysis based on dynamic time warping
2013Co-Authors: Dong Zhe, Tie Wang, Andrew AllAbstract:Electrical motor stator current signals have been widely used to monitor the condition of induction machines and their downstream mechanical equipment. The key technique used for current signal analysis is based on Fourier transform (FT) to extract weak fault sideband components from signals predominated with Supply Frequency component and its higher order harmonics. However, the FT based method has limitations such as spectral leakage and aliasing, leading to significant errors in estimating the sideband components. Therefore, this paper presents the use of dynamic time warping (DTW) to process the motor current signals for detecting and quantifying common faults in a downstream two-stage reciprocating compressor. DTW is a time domain based method and its algorithm is simple and easy to be embedded into real-time devices. In this study DTW is used to suppress the Supply Frequency component and highlight the sideband components based on the introduction of a reference signal which has the same Frequency component as that of the Supply power. Moreover, a sliding window is designed to process the raw signal using DTW frame by frame for effective calculation. Based on the proposed method, the stator current signals measured from the compressor induced with different common faults and under different loads are analysed for fault diagnosis. Results show that DTW based on residual signal analysis through the introduction of a reference signal allows the Supply components to be suppressed well so that the fault related sideband components are highlighted for obtaining accurate fault detection and diagnosis results. In particular, the root mean square (RMS) values of the residual signal can indicate the differences between the healthy case and different faults under varying discharge pressures. It provides an effective and easy approach to the analysis of motor current signals for better fault diagnosis of the downstream mechanical equipment of motor drives in the time domain in comparison with conventional FT based methods.
