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.

Broken Rotor Bar Fault Diagnosis of Induction Motors Using a Hybrid Bare-bones Particle Swarm Optimization Algorithm

在传统定子电流频谱分析中,感应电机转子断条故障特征经常被基波分量淹没而无法准确检测。针对该问题,提出一种基于混合骨干微粒群优化算法的转子断条故障诊断新方法。该方法首先根据电流信号与单位余弦基函数的内积最大准则,利用混合骨干微粒群算法强大的全局搜索能力,准确估计出基波波形参数;然后利用波形参数构造出基波表达式,并将其从原电流信号中剔除,达到突出故障特征的目的。针对微粒群算法在进化后期收敛缓慢的缺点,通过K–均值聚类方式,引入单纯形法对其进行改进,使整个算法的广度探索与深度开发能力得到了有效均衡。最后,对模拟数据和实测信号进行实验,结果验证了所提方法的有效性和优越性。

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.

A Fault Diagnosis Approach for Broken Rotor Bars Based on EMD and Envelope Analysis

Empirical Mode Decomposition (EMD) used to deal with non-linear and non-stable signals,is a time-frequency analytical method that has been developed recently. In this paper the EMD method is used to filter the noise from the stator current signal that arises when rotor bars break. Then a Hilbert Transform is used to extract the envelope from the filtered signal. With the EMD method again,the frequency band containing the fault characteris-tic-frequency components,2sf,can be extracted from the signal's envelope. The last step is to use a Fast Fourier Trans-form (FFT) method to extract the fault characteristic frequency. This frequency can be detected in actual data from a faulty motor,as shown by example. Compared to the Extend Park Vector method this method is proved to be more sen-sitive under light motor load.

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.

供电电压闪变对异步电动机转子故障在线检测的影响及应对

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。

基于小波包能量分析和信号融合的异步电机转子故障诊断

为提高异步电机转子故障诊断的可靠性,文中介绍了一种基于小波包能量分析和信号融合的异步电机转子故障诊断方法。采用定子电流信号和振动信号的频谱特征融合作为转子断条以及气隙偏心故障的诊断依据,首先对信号进行小波包分解,获得不同小波包频带节点下对应的能量分布,并与正常电机信号进行比较,进而对能量异常的信号频段进行小波包节点重构,最后通过快速傅里叶变换识别故障特征频率,诊断电机故障是否发生。通过仿真分析,验证了该方法的有效性和实用性,对于电机运行状态的准确监测具有重要意义。

融合TKEO和Goerztel算法的感应电机转子断条故障诊断

针对电机电流信号特征分析进行转子断条故障诊断时,在低负载工况或早期,转子断条故障存在故障特征微弱以及难以分离等问题,同时需借助速度传感器获取转速信息,增加了硬件成本,该文提出了融合Teager-Kaiser能量算子(TKEO)和Goertzel算法的感应电机转子断条故障诊断方法。首先,采用Teager-Kaiser能量算子对定子电流进行解调处理,将基频分量转化为直流分量并去除,实现边带故障特征分量到转子断条故障特征分量以及固有偏心谐波分量到转子速度谐波分量的转变,抑制基频频谱泄露对故障诊断和转速估计造成的干扰;其次,利用Goertzel算法对归一化解调信号进行频谱分析,完成转子断条故障特征分量和转子速度谐波分量的快速提取,进而实现不同负载和不同严重程度工况下转子断条的故障诊断。此外,与归一化频域能量算子进行比较,分析其计算效率和稳定性。实验结果表明,所提方法在不同负载和不同严重程度工况下均能够有效地识别转子断条故障特征分量,并且计算量少、可靠性高、实时性强以及硬件成本低。

基于ESPRIT-PSA与LightGBM算法的感应电动机转子断条数目诊断新方法

提出一种基于旋转不变信号参数估计技术ESPRIT(Estimation of signal parameters via rotational invariance technique)、模式搜索算法PSA(Pattern search algorithm)与轻型梯度提升机LightGBM(Light gradient boosting machine)结合的感应电动机转子断条数目诊断新方法。模拟了转子断条故障下的瞬时无功功率信号并用其衡量ESPRIT-PSA的性能。结果表明:ESPRIT-PSA只需短时数据就能准确测量瞬时无功功率信号中的转子断条故障特征分量。随后,为解决现有的电机瞬时无功功率信号分析MIRPSA(Motor instantaneous reactive power signal analysis)类方法无法准确诊断转子断条数目的问题,引入LightGBM对转子断条故障进行多分类以准确诊断转子断条数目。最后针对一台异步电动机进行转子断条诊断实验,结果表明:该方法是有效的,并且因将瞬时无功功率作为分析信号而适用于电机低转差率的情况。

高速列车牵引电机转子断条和速度传感器联合诊断方法

为提升高速列车牵引系统的稳定性和可靠性,针对其牵引电机提出一种基于未知输入观测器的转子断条和速度传感器故障联合诊断方法.首先,通过非奇异坐标变换,将牵引电机系统解耦为两个分别只包含转子断条故障和速度传感器故障的子系统,实现转子断条故障与速度传感器故障的解耦,并进一步利用一阶低通滤波器将含速度传感器故障的子系统转化为增广系统.其次,对含转子断条故障的子系统和速度传感器故障增广系统分别设计未知输入区间观测器和未知输入滑模观测器.在此基础上,采用未知输入区间观测器上界和下界构建转子断条故障诊断的检测变量和自适应阈值,利用未知输入滑模观测器的等效输出控制原理实现速度传感器故障估计.最后,通过仿真和TDCS-FIB平台实验验证了所提方法的有效性和鲁棒性.

三相鼠笼异步电机转子断条故障检测

针对转子断条故障信号易被基波淹没,故障特征不易提取及容易误判等问题,提出在空载低压运行状态下通过基于奇异值增长率的SVD-Prony算法检测转子断条故障。在研究转子断条故障特征及转子槽谐波测速机理的基础上,给出了整数倍转子槽数的理论计算公式及转子断条故障信号频率与转子槽谐波频率的关系表达式,并通过转子槽谐波频率验证转子断条故障信号的正确性,以此提高转子断条故障检测的有效性。通过仿真,验证了所提方法的有效性和实用性,对电机转子断条故障检测研究具有重要意义。

基于故障特征分析的笼型电机转子断条故障诊断方法综述

笼型电机在运行过程中,电气、机械、热、磁和环境应力均可能导致其出现故障。开发一种可靠且高效的笼型电机故障诊断技术逐渐成为一项重要的研究课题。转子断条故障是笼型电机关键故障之一,会影响笼型电机的正常工作,需要对转子断条进行监测,及时发现潜在故障并预警。针对该问题,从电流信号、电压信号、振动信号和声信号这几方面进行分析,对现有的转子断条故障诊断技术进行梳理、归纳,并将各类方法的优势和局限性进行对比分析,总结了笼型电机转子断条故障诊断技术存在的主要问题,并对未来发展趋势进行了展望,旨在给该领域研究人员提供研究思路。

升船机驱动电机匝间短路与断条故障诊断

为保证升船机的可靠运行,降低由电机故障引起升船机系统性瘫痪的风险,文章对驱动电机匝间短路与转子断条故障进行仿真分析并提出诊断方法。首先根据驱动电机相关参数对驱动电机进行设计,采用Maxwell RMxprt对电机进行参数化建模,并生成Maxwell 2D模型,验证模型的正确性与合理性。然后分析匝间短路与转子断条故障的产生机理,设计故障电机模型,对正常与故障电机模型进行仿真,对比分析电机在正常与故障状态下各参数的变化。最后采用Park变换及SVM分别对驱动电机匝间短路和转子断条进行诊断。结果表明所设计的诊断方案具有准确性与可靠性。

异步电动机转子断条失效诊断研究与发展概况

介绍了异步电动机转子断条故障下对电机性能的影响分析,以及当前主流故障分析原理和实现途径,最终为转子断条故障诊断提供了指南,为大中型异步电动机稳定运行和维护提供了重要参考。

基于RELAX频谱分析方法的鼠笼式异步电动机转子故障诊断

在基于电流信号分析的异步电机故障诊断方法中,故障特征成分(尤其是断条特征)往往容易被基波分量和噪声信号中所淹没。因此,有效地克服基波和噪声的影响是诊断过程的关键。RELAX是一种对加性噪声以及系统误差假设可松弛的算法,提出了一种基于该算法的鼠笼式异步电动机转子故障监测与诊断方法。文中从理论上推导了鼠笼式异步电动机转子故障时的电流信号模型,将RELAX算法应用于电源基波特征参数提取,并从噪声和杂波的连续谱中估计出特定故障的离散谱参数,从而达到消除电源和噪声影响、突出故障特征的目的。通过对样机实测信号进行了分析处理,实验结果验证了RELAX算法的有效性和优越性。

基于Hilbert模量频谱分析的异步电机转子断条故障研究

转子绕组断条是异步电机最常见故障之一。在分析当前主要诊断方法的基础上,提出Hilbert模量频谱分析方法对异步电机转子断条故障进行研究。将Hilbert模量定义为原始信号与其共轭信号的平方和,利用Hilbert模量将原相电流中的基波分量转变为直流分量,原频谱分析的转绕组断条的故障特征分量转变为频率为二倍转差频率的电流分量,决了相电流频谱分析方法中断条故障特征成分容易被基波湮没而难以突出故障特征的问题。文中研究了Hilbert模量频谱分析方法的原理和实现方法,并对偏心故障电机和转子断条电机分别进行仿真和实验研究。仿真分析和实验结果均表明,该方法对异步电机转子断条故障诊断具有较高的诊断精度和很好的频率分辨率,能克服传统的电流频谱法主频泄漏的影响,且工程实现方便。

基于双PQ变换的感应电机转子故障诊断

提出了基于双PQ变换的感应电机转子故障诊断新方法。该方法以三相基波正序电压及其希尔伯特变换构造PQ变换矩阵,用其对定子三相电流进行变换,得到电机有功功率P及无功功率Q。由于正常电机P、Q均恒定,在PQ坐标系下对应1个点;故障电机则由于P、Q中附加频率为2sfs(s为转差率,fs为电网频率)的波动分量而对应于1个椭圆。通过比较电机正常及转子断条故障运行时的P、Q在PQ坐标系下对应的模式,可以实现转子断条故障的可靠检测。选择转子断条电机在PQ坐标系下对应椭圆的长轴为故障特征,椭圆中心与空载运行点之间的距离为归一化基准值,得到了1个近似独立于电机惯量及负载等级的故障严重因子。实验结果证明了该方法的有效性。

基于改进多信号分类法的异步电机转子故障特征分量的提取

在基于定子电流信号进行异步电机故障诊断时,转子断条故障特征频率分量常常被电流的基频分量淹没。针对这一情况,该文提出一种新的改进的MUSIC方法来提取这一故障特征频率。MUSIC方法通过特征值分解把自相关矩阵中包含的信息空间分成信号子空间和噪声子空间两个正交的子空间,该文提出的改进方法是将信号子空间中对应最大主分量的特征向量移到噪声子空间,这样构成两个新的正交子空间I和Ⅱ。子空间I由信号中的最大主分量和噪声所对应的特征向量张成,子空间Ⅱ由其他分量的特征向量张成。把不同频率的信号投影到子空间I,基频信号在该空间的投影将远大于其他的频率分量,因此在投影的倒数谱中,基频分量被抑制,凸显出了故障频率分量。仿真和实验表明,该方法用于提取转子断条故障特征是可行并且是有效的。

利用相关性基波消去法诊断电机转子断条故障

针对感应电机转子故障时,定子电流中新增的故障特征分量被基波信息淹没而难以识别的问题,提出基于相关性基波消去法来检测转子断条故障。根据定子电压与电流基波频率相等的特点,构造出与定子电压同频的参考信号,利用改进的相关算法提取定子电流基波信号的幅值与相位,将基波分量准确滤除来突出转子断条故障信息,然后对滤波后的故障信号作频谱分析,可容易诊断出转子断条故障。该方法无需复杂的坐标变换,计算量小,实时性大大提高。实验结果表明:该方法具备较强的故障辨识能力,即使在轻载的情况下仍能有效对转子故障作出诊断。另外,系统只需采样单相电流信号和电压频率信号,工程开销小,易于实现。

考虑负荷及惯量影响的感应电机转子故障诊断

该文主要目的是分析感应电机转子故障诊断中两个关键问题:①如何定义独立于负载等级及惯量大小的故障严重因子;②转子故障与负荷波动的区分。详细推导了计及速度波动时转子断条故障在定子电流中引入的左右边频分量(1±2s)fs(s为转差率,fs为电网频率)的表达式,并指出其相位之间存在一定的关系。根据负荷波动及转子断条电机特征电流在dcqc坐标系下对应椭圆方向的不同,实现了二者的区分。以仅由转子故障引入的电流分量幅值为故障特征给故障程度1个更精确的量化指标,即得到1个独立于电机负载等级及惯量大小的故障严重因子。对1台4kW电机进行实验,结果证明了该方法的正确性。