Online Detection of Broken Rotor Bar Fault in Induction Motors by Combining Estimation of Signal Parameters via Min-norm Algorithm and Least Square Method

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.

Fourier and wavelet transformations application to fault detection of induction motor with stator current

Fault detection of an induction motor was carried out using the information of the stator current. After synchronizing the actual data, Fourier and wavelet transformations were adopted in order to obtain the sideband or detail value characteristics under healthy and various faulty operating conditions. The most reliable phase current among the three phase currents was selected using an approach that employs the fuzzy entropy measure. Data were trained with a neural network system, and the fault detection algorithm was verified using the unknown data. Results of the proposed approach based on Fourier and wavelet transformations indicate that the faults can be properly classified into six categories. The training error is 5.3×10-7, and the average test error is 0.103.

IBPSO-Based MUSIC Algorithm for Broken Rotor Bars Fault Detection of Induction Motors

In spectrum analysis of induction motor current, the characteristic components of broken rotor bars（BRB） fault are often submerged by the fundamental component. Although many detection methods have been proposed for this problem, the frequency resolution and accuracy are not high enough so that the reliability of BRB fault detection is a ected. Thus, a new multiple signal classification（MUSIC） algorithm based on particle swarm intelligence search is developed. Since spectrum peak search in MUSIC is a multimodal optimization problem, an improved bare?bones particle swarm optimization algorithm（IBPSO） is proposed first. In the IBPSO, a modified strategy of subpopulation determination is introduced into BPSO for realizing multimodal search. And then, the new MUSIC algorithm, called IBPSO?based MUSIC, is proposed by replacing the fixed?step traversal search with IBPSO. Meanwhile, a simulation signal is used to test the e ectiveness of the proposed algorithm. The simulation results show that its frequency precision reaches 10-5, and the computational cost is only comparable to that of traditional MUSIC with 0.1 search step. Finally, the IBPSO?based MUSIC is applied in BRB fault detection of an induction motor, and the e ectiveness and superiority are proved again. The proposed research provides a modified MUSIC algorithm which has su cient frequency precision to detect BRB fault in induction motors.

Broken Rotor Bar Fault Detection of Induction Motors Using a Joint Algorithm of Trust Region and Modified Bare-bones Particle Swarm Optimization

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.

An Adaptive EMD Technique for Induction Motor Fault Detection

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.

A new measuring technique in detecting of high voltage three phase induction motor for single phasing

Open phase in three phase induction motors is a common fault that can occur as a result of a fuse blowing or a pro- tective device failing on one phase of the motor. This paper introduces a new method,which is based on the transient mea- surement and can distinguish the fault of one phase connecting ground. The method has been proved to be in correspondence with the simulation results by Matlab and LabVIEW in practice, The method has merits of simplicity, accuracy and ease of USe.

Compensation of Dead-Time Effect of Speed Sensorless Vector Control System

The key of speed sensorless vector control system lies in the accurate orientation of magnetic field. In some field-oriented algorithms, the integrator of observers and the dead-time effect bring in system errors during the estimation of field position. In this paper, a saturated feedback integrator is used, and the dead-time effect is compen- sated by current positive feedback. Experiments were carried out on the hardware platform of MCK2407, with chip TMS320LF2407 from TI Company. The results show that the prooosed method is simole and effective, and the accuracy of field position is improved.

Robust sensor faults detection for induction motor using observer

Current sensor is one of the key elements in the control system of induction motor. Whether the accurate measurement of variables reflecting motor operation status can be made will directly affect the control effect on motor system and therefore the timely, accurate detection of sensor fault is necessary. This paper brings forward an observer- based method of residual generation and fault detection on the basis of the mathematical model of the induction motor. As whether or not the nonlinear part satisfies the Lipschitz conditions does not limit the observer design, the application of such an observer is expanded. Meanwhile, the contradiction between robust error and fault sensitivity is also settled. The correctness and effectiveness of such method are verified by experimental testing on the simulated fault which also casts light on engineering practice.

Thorough Validation of a Rotor Fault Diagnosis Methodology in Laboratory and Field Soft-Started Induction Motors

Induction motors are the most widespread rotating electrical machines in industry.Predictive maintenance of the motors is of crucial importance due to the fact that unexpected faults in those machines can lead to huge economic losses for the corresponding companies.Over recent years,there is an increasing use of industrial induction motors operated by different types of drives,which have different functionalities.Among them,the use of soft-starters has proliferated due to the inherent benefits provided by these drives:they damp the high starting currents,enabling the soft startup of the motors and avoiding undesirable commutation transients introduced by other starting modalities.In spite of these advantages,they do not avoid the possible occurrence of rotor damages,one of the most common faults in this type of motors.Few works have proposed predictive maintenance techniques that are aimed to diagnose the rotor condition in soft-started machines and even fewer have demonstrated the validity of their methods in real motors.This work presents,for the first time,the massive validation of a rotor fault diagnosis methodology in soft-started induction motors.Industrial and laboratory and induction motors started under different types of soft-starters and with diverse rotor fault conditions are considered in the work.The results prove the potential of the approach for the reliable assessment of the rotor condition in such machines.

笼型异步电动机转子断条故障检测新方法

计及实际电机本身所固有的非对称、气隙偏心、转子不对中及其它因素,进行高灵敏度、高可靠性的笼型异步电动机转子断条故障检测,是一个亟待解决并颇具工程意义的研究课题,该文即以此作为研究重心。首先完成了笼型异步电动机转子断条故障数字仿真与物理实验。通过分析仿真与实验结果,总结出在转子断条故障检测实践中保证其灵敏度与可靠性的原则及方法。在此基础上,首次将连续细化傅里叶变换、自适应滤波、转子齿槽谐波转差率估计、检测阈值自整定技术有机结合,形成了笼型异步电动机转子断条故障检测新方法。实验结果表明,该方法是切实可行的。

基于MUSIC与SAA的笼型异步电动机转子断条故障检测

提出了一种基于多重信号分类(Multiple Signal Classification,MUSIC)与模拟退火算法(Simulated Annealing Algorithm,SAA)的异步电动机转子断条故障检测新方法。首先以转子断条故障仿真信号检验MUSIC性能,结果表明:MUSIC对于短时信号具备高频率分辨力,可以准确计算转子断条故障特征分量以及其他分量之频率;但对诸频率分量幅值、初相角,MUSIC无能为力。为此,引入SAA确定诸频率分量幅值、初相角,效果理想。进而,对一台Y100L—2型3kW笼型异步电动机完成了转子断条故障检测实验。实验结果表明:基于MUSIC与SAA的异步电动机转子断条故障检测方法是切实可行的,并且因仅需处理短时信号而适用于负荷波动、噪声等干扰严重情况。

无位置传感器的SRD调速系统的初始位置检测

该文基于对SRM线性电感模型和连续两相电磁特性进行分析的基础上,探讨了转子处于静止和有初始转动两种状态下,转子初始位置检测及初始导通相的确定方法,通过对6/4极的三相开关磁阻电机的实验,验证了该方法的可行性。

基于DSO-2100的异步电机转子偏心故障检测与诊断

为了诊断感应电动机转子偏心故障,采用了定子电流检测法,开发出了一套基于DSO-2100虚拟数字示波器的故障诊断系统,得出了当电机转子存在偏心故障时,定子电流频谱中的特征频率值在不同偏心程度和不同负载下的变化规律,并完成了数据采集模块的设计。试验结果证明,诊断的结果与理论值完全相符。

基于观测器的感应电机故障检测方法及应用

定子绕组和转子绕组的故障是导致感应电机失效的主要原因之一,实时监测电机运行状况不仅可以提高电机运行的可靠性,而且可以避免不必要的经济损失,因此及时而有效地检测感应电机绕组故障是完全有必要的。首先对感应电机定子绕组和转子绕组的故障特性进行分析,并对其故障进行了建模,然后利用感应电机d-q坐标系的状态空间数学模型,提出了一种鲁棒观测器的设计方法,该方法不仅对未知输入扰动具有良好的鲁棒性,而且对绕组故障具有很高的灵敏度,最后对模拟的绕组故障进行了实验,结果证实该方法是正确有效的。同时该方法的提出对实际工程应用也具有一定的参考价值。

基于Clarke变换的感应电机定子故障检测方法

提出了一种基于 Clarke变换的感应电机定子线圈短路故障检测方法 ,根据 Clarke矢量模平方函数导出了与线圈短路故障相关的故障特征 ,定义了表征故障程度的灵敏因子 λ。实验结果证实 ,将该方法应用于感应电机定子线圈故障检测 ,可准确检测出反映感应电机在定子线圈短路时的故障特征 ,方法实际可行。

基于多重信号分类与模式搜索算法的笼型异步电动机转子断条故障检测新方法

提出了一种基于多重信号分类(multiple signal classification,MUSIC)与模式搜索算法(pattern search algorithm,PSA)的异步电动机转子断条故障检测新方法。MUSIC方法对于短时信号具备高频率分辨力,可以准确计算转子断条故障特征分量以及其他分量的频率;但对诸频率分量幅值和初相角则无法准确求解。因此引入PSA确定诸频率分量的幅值、初相角,并对1台Y100L-2型3 kW笼型异步电动机完成了转子断条故障检测实验。实验结果表明:基于MUSIC与PSA的异步电动机转子断条故障检测方法切实可行,适用于负荷波动、噪声等干扰严重情况。

基于定子电流希尔伯特模式的笼型异步电动机转子断条在线检测方法

利用图形缩放技术 ,对笼型异步电动机转子正常及断条情况下的定子电流信号希尔伯特模式做了研究 ,并概括出内在规律。以此为基础 ,提出了一种基于定子电流希尔伯特模式的笼型异步电动机转子断条的线检测方法。

死区对电压型逆变器输出误差的影响及其补偿

针对电压型逆变器的死区效应,分析了死区对逆变器输出电压谐波和电流的影响,提出一种基于功率导通时间实时检测的死区补偿和电机相电压检测方法。为避免电流过零点的直接检测或间接预估,设计硬件电路直接检测功率管的导通时间,得到逆变器的实际死区时间,并在软件中进行补偿,同时得到电机实际的相电压。采用该方法得到的死区时间反映了开关器件导通和关断时间受结温和导通电流影响的特性,最后对上述方法进行了实验验证。

异步电动机定子绕组匝间短路故障检测新方法

定子绕组匝间短路是异步电动机常见故障之一,因此研究其检测方法具有重要意义。针对异步电动机定子绕组匝间短路与转子断条故障瞬态进行仿真并分析仿真结果,指出异步电动机转子故障对定子绕组匝间短路故障检测存在不利影响,甚至导致故障误判。提出计及转子故障时的异步电动机定子绕组匝间短路故障检测新方法,该方法关键在于预先采用频谱校正与自适应滤波技术滤除定子电流中由转子故障所导致的特征频率分量。大量仿真与实验结果表明,该方法可以避免将转子故障误判为定子绕组匝间短路故障,使定子绕组匝间短路故障识别可靠性大幅提高。

基于虚拟仪器技术的异步电机定子匝间短路故障在线监测系统

为实现对异步电机定子匝间短路故障进行在线监测,提出了一种基于虚拟仪器技术的故障监测系统设计方法。以高性能ARM微处理器S3C2410X作为电机物理量采集的核心,利用LabVIEW7.0虚拟仪器开发平台计算电机定子负序电流,并考虑电机绕组先天不平衡因素,定义了故障灵敏度因子反映匝间短路故障程度。实验结果表明:该系统有较好的可靠性和实时性,并具备较强的匝间短路故障辨识能力,方法可行。