Current research in broken rotor bar （BRB） fault detection in induction motors is primarily focused on a high-frequency resolution analysis of the stator current. Compared with a discrete Fourier transformation, the...Current research in broken rotor bar （BRB） fault detection in induction motors is primarily focused on a high-frequency resolution analysis of the stator current. Compared with a discrete Fourier transformation, the parametric spectrum estimation technique has a higher frequency accuracy and resolution. However, the existing detection methods based on parametric spectrum estima- tion cannot realize online detection, owing to the large computational cost. To improve the efficiency of BRB fault detection, a new detection method based on the min-norm algorithm and least square estimation is proposed in this paper. First, the stator current is filtered using a band-pass filter and divided into short overlapped data windows. The min-norm algorithm is then applied to determine the fre- quencies of the fundamental and fault characteristic com- ponents with each overlapped data window. Next, based on the frequency values obtained, a model of the fault current signal is constructed. Subsequently, a linear least squares problem solved through singular value decomposition is designed to estimate the amplitudes and phases of the related components. Finally, the proposed method is applied to a simulated current and an actual motor, the results of which indicate that, not only parametric spectrum estimation technique.展开更多
A precise detection of the fault feature parameter of motor current is a new research hotspot in the broken rotor bar(BRB) fault diagnosis of induction motors. Discrete Fourier transform(DFT) is the most popular techn...A precise detection of the fault feature parameter of motor current is a new research hotspot in the broken rotor bar(BRB) fault diagnosis of induction motors. Discrete Fourier transform(DFT) is the most popular technique in this field, owing to low computation and easy realization. However, its accuracy is often limited by the data window length, spectral leakage, fence e ect, etc. Therefore, a new detection method based on a global optimization algorithm is proposed. First, a BRB fault current model and a residual error function are designed to transform the fault parameter detection problem into a nonlinear least-square problem. Because this optimization problem has a great number of local optima and needs to be resolved rapidly and accurately, a joint algorithm(called TR-MBPSO) based on a modified bare-bones particle swarm optimization(BPSO) and trust region(TR) is subsequently proposed. In the TR-MBPSO, a reinitialization strategy of inactive particle is introduced to the BPSO to enhance the swarm diversity and global search ability. Meanwhile, the TR is combined with the modified BPSO to improve convergence speed and accuracy. It also includes a global convergence analysis, whose result proves that the TR-MBPSO can converge to the global optimum with the probability of 1. Both simulations and experiments are conducted, and the results indicate that the proposed detection method not only has high accuracy of parameter estimation with short-time data window, e.g., the magnitude and frequency precision of the fault-related components reaches 10^(-4), but also overcomes the impacts of spectral leakage and non-integer-period sampling. The proposed research provides a new BRB detection method, which has enough precision to extract the parameters of the fault feature components.展开更多
Reliable induction motor (IM) fault detection techniques are very useful in industries to diagnose IM defects and improve operational performance. An adaptive empirical mode decomposition (EMD) technology is proposed ...Reliable induction motor (IM) fault detection techniques are very useful in industries to diagnose IM defects and improve operational performance. An adaptive empirical mode decomposition (EMD) technology is proposed in this paper for rotor bar fault detection in IMs. As the characteristic fault frequency will change with operating conditions related to load and speed, the proposed adaptive EMD technique correlates fault features over different frequency bands and intrinsic mode function (IMF) sidebands. The adaptive EMD technique uses the first IMF to detect the fault type and the second IMF as an indicator to predict the fault severity. It can overcome the problems of the sensitivity of sideband frequencies related to the speed and load oscillations. The effectiveness of the proposed adaptive EMD technique is verified by experimental tests under different motor conditions.展开更多
供电电压闪变可能对异步电动机转子故障在线检测产生不利影响,导致基于定子电流信号分析(motor current signature analysis,MCSA)的转子故障在线检测方法失效。通过理论分析,揭示供电电压闪变恶化转子故障在线检测性能的机制。提出免...供电电压闪变可能对异步电动机转子故障在线检测产生不利影响,导致基于定子电流信号分析(motor current signature analysis,MCSA)的转子故障在线检测方法失效。通过理论分析,揭示供电电压闪变恶化转子故障在线检测性能的机制。提出免于供电电压闪变影响的异步电动机转子故障在线检测方法,首先,根据转子故障主特征频率分量预判转子健康或故障;继而,根据转子故障独有的辅助特征频率分量进一步确认转子健康或故障。仿真与实验结果证明了该方法的有效性。展开更多
受基频频谱泄露影响,经典MCSA方法诊断鼠笼电机转子断条故障时的诊断能力严重依赖于电机负载大小。针对这一问题,提出了基于定子电流信号平方解调制分析诊断方法。首先采用硬件方式对定子电流信号作基于平方解调制的信号预处理,以此消...受基频频谱泄露影响,经典MCSA方法诊断鼠笼电机转子断条故障时的诊断能力严重依赖于电机负载大小。针对这一问题,提出了基于定子电流信号平方解调制分析诊断方法。首先采用硬件方式对定子电流信号作基于平方解调制的信号预处理,以此消除制约诊断能力的基频频谱泄露,继而对解调后的信号作快速傅里叶变换,然后根据频谱中是否存在特征频率成分判断转子断条故障发生与否。在3 k W电机实验平台上对所提出的方法进行实验验证。实验结果表明,即使鼠笼电机在轻载或空载条件下运行时所提出的方法仍然能够诊断出转子断条故障,从而有效提高了诊断能力。展开更多
基金Supported by National Natural Science Foundation of China(Grant No.51607180)
文摘Current research in broken rotor bar （BRB） fault detection in induction motors is primarily focused on a high-frequency resolution analysis of the stator current. Compared with a discrete Fourier transformation, the parametric spectrum estimation technique has a higher frequency accuracy and resolution. However, the existing detection methods based on parametric spectrum estima- tion cannot realize online detection, owing to the large computational cost. To improve the efficiency of BRB fault detection, a new detection method based on the min-norm algorithm and least square estimation is proposed in this paper. First, the stator current is filtered using a band-pass filter and divided into short overlapped data windows. The min-norm algorithm is then applied to determine the fre- quencies of the fundamental and fault characteristic com- ponents with each overlapped data window. Next, based on the frequency values obtained, a model of the fault current signal is constructed. Subsequently, a linear least squares problem solved through singular value decomposition is designed to estimate the amplitudes and phases of the related components. Finally, the proposed method is applied to a simulated current and an actual motor, the results of which indicate that, not only parametric spectrum estimation technique.
基金Supported by Fundamental Research Funds for the Central Universities(Grant No.2017XKQY032)
文摘A precise detection of the fault feature parameter of motor current is a new research hotspot in the broken rotor bar(BRB) fault diagnosis of induction motors. Discrete Fourier transform(DFT) is the most popular technique in this field, owing to low computation and easy realization. However, its accuracy is often limited by the data window length, spectral leakage, fence e ect, etc. Therefore, a new detection method based on a global optimization algorithm is proposed. First, a BRB fault current model and a residual error function are designed to transform the fault parameter detection problem into a nonlinear least-square problem. Because this optimization problem has a great number of local optima and needs to be resolved rapidly and accurately, a joint algorithm(called TR-MBPSO) based on a modified bare-bones particle swarm optimization(BPSO) and trust region(TR) is subsequently proposed. In the TR-MBPSO, a reinitialization strategy of inactive particle is introduced to the BPSO to enhance the swarm diversity and global search ability. Meanwhile, the TR is combined with the modified BPSO to improve convergence speed and accuracy. It also includes a global convergence analysis, whose result proves that the TR-MBPSO can converge to the global optimum with the probability of 1. Both simulations and experiments are conducted, and the results indicate that the proposed detection method not only has high accuracy of parameter estimation with short-time data window, e.g., the magnitude and frequency precision of the fault-related components reaches 10^(-4), but also overcomes the impacts of spectral leakage and non-integer-period sampling. The proposed research provides a new BRB detection method, which has enough precision to extract the parameters of the fault feature components.
文摘Reliable induction motor (IM) fault detection techniques are very useful in industries to diagnose IM defects and improve operational performance. An adaptive empirical mode decomposition (EMD) technology is proposed in this paper for rotor bar fault detection in IMs. As the characteristic fault frequency will change with operating conditions related to load and speed, the proposed adaptive EMD technique correlates fault features over different frequency bands and intrinsic mode function (IMF) sidebands. The adaptive EMD technique uses the first IMF to detect the fault type and the second IMF as an indicator to predict the fault severity. It can overcome the problems of the sensitivity of sideband frequencies related to the speed and load oscillations. The effectiveness of the proposed adaptive EMD technique is verified by experimental tests under different motor conditions.
文摘供电电压闪变可能对异步电动机转子故障在线检测产生不利影响,导致基于定子电流信号分析(motor current signature analysis,MCSA)的转子故障在线检测方法失效。通过理论分析,揭示供电电压闪变恶化转子故障在线检测性能的机制。提出免于供电电压闪变影响的异步电动机转子故障在线检测方法,首先,根据转子故障主特征频率分量预判转子健康或故障;继而,根据转子故障独有的辅助特征频率分量进一步确认转子健康或故障。仿真与实验结果证明了该方法的有效性。
文摘受基频频谱泄露影响,经典MCSA方法诊断鼠笼电机转子断条故障时的诊断能力严重依赖于电机负载大小。针对这一问题,提出了基于定子电流信号平方解调制分析诊断方法。首先采用硬件方式对定子电流信号作基于平方解调制的信号预处理,以此消除制约诊断能力的基频频谱泄露,继而对解调后的信号作快速傅里叶变换,然后根据频谱中是否存在特征频率成分判断转子断条故障发生与否。在3 k W电机实验平台上对所提出的方法进行实验验证。实验结果表明,即使鼠笼电机在轻载或空载条件下运行时所提出的方法仍然能够诊断出转子断条故障,从而有效提高了诊断能力。
