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.

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 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。

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Attention-CNN的振动信号电机转子断条识别

针对基于振动信号的转子断条诊断技术依赖于人工特征选择,泛化能力差,以及常规卷积神经网络(Convolution neural network,CNN)模型在对时序信号自动特征提取时忽略序列信息的问题,利用Attention机制对局部特征在整体表达时的度量能力,提出了一种Attention-CNN网络模型。首先通过Attention在原始信号上分配注意力,其次结合CNN进行特征提取构建网络,然后利用粒子群优化算法(Particle swarm optimization,PSO)进行网络超参数寻优,训练转子断条识别模型,最后从整体和局部两个方面进行模型评价。试验结果表明,所提出的识别模型能够达到传统诊断水平,且泛化能力高于现有方法,更适用于通过振动信号进行电机转子断条识别。

一起火电厂循环水泵电动机电流波动案例分析

高压异步电动机常见的故障有转子断条裂纹及偏心,故障时定子电流表现出持续波动的特征。本文对某600MW级火电厂2B循环水泵电动机电流进行时域及频域计算与分析,确认电动机转子存在断条裂纹缺陷,并利用广义解调法对电动机电流特征分析(MCSA)结果进行验证;利用转子存在缺陷或故障时定子电流波动的特征,建立基于分布式控制系统(DCS)运行数据的异步电动机电流波动预警算法,并利用该故障电动机的历史电流数据进行验证,结果表明电动机转子断条的持续劣化能够有效地触发预警。

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。

基于骨干微粒群算法和支持向量机的电机转子断条故障诊断

为了准确识别感应电机转子断条故障,本文提出一种基于骨干微粒群算法和支持向量机的故障诊断新方法,并给出了可行的诊断步骤和分析。首先根据故障电流信号的特点,提出一种基于骨干微粒群算法的基波滤除方法,用以消除基波分量对故障特征的影响。然后利用小波包频带能量分解技术,将残余电流信号分解到不同频带,形成感应电机运行状态的特征向量,并以此作为支持向量机的输入向量。采用"一对一"向量机进行分类,并利用骨干微粒群算法和交叉检验优化支持向量机模型参数。最后实验结果表明,该方法诊断感应电机转子断条故障能取得良好的效果。

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。

基于电磁转矩小波变换的感应电机转子断条故障诊断

该文提出了一种基于感应电机启动电磁转矩信号的转子断条故障诊断新方法。利用复值小波变换中的小波脊线提取出启动电磁转矩中故障特征在电机启动过程中的频率变化规律,检测出转子断条故障。同时,小波系数的模反映了信号在对应尺度上的能量大小,所以对应故障特征脊线上的小波系数模反映了该故障特征转矩的幅值变化规律。基于此,定义了脊线-小波能量谱的概念。将故障特征转矩对应的脊线-小波能量谱作为故障严重程度因子,即可根据其与转子连续断条根数的关系给出感应电机转子故障严重程度的表示方法。实验结果证明该方法是可行的。

短时数据下利用双HTLS参数估计的异步电机转子断条故障诊断

传统基于傅里叶分析的算法,其频谱分辨率受限于数据长度,对此,提出一种在短时数据下利用双HTLS算法进行异步电机转子断条故障诊断的方法。利用HTLS算法中的Hankel矩阵左奇异子矩阵的平移不变性质,准确估算出基波幅值、频率、相位参数;然后利用基波参数构造基波表达式,将其从定子电流原始信号中剔除,从而得到残余电流;对残余电流再次进行HTLS参数估计,辨识出故障成分的参数,并有效抑制噪声的影响。对不同数量的转子断条故障进行实验验证,结果表明双HTLS算法具有高鲁棒性的优点,且适用于噪声背景及短时数据下的转子断条故障检测。

笼型异步电动机转子断条数目诊断新判据

笼型异步电动机一旦发生转子断条故障,在其定子电流中将出现边频分量。当前已提出Filippetti判据等基于定子电流边频分量的转子断条数目诊断判据,但其准确性有待改进。该文从定子电流边频分量的产生机理入手,推导出转子断条数目诊断新判据。相对于Filippetti判据而言,新判据具有普遍性,可以视为Filippetti判据的推广。新判据同时考虑了导条间电流对转子断条数目诊断的影响。针对一台Y100L-2型三相异步电动机(3kW,380V,50Hz)完成了转子断条故障实验。实验结果表明新判据在准确性方面得以改进。

Morlet小波解调制方法诊断鼠笼电机转子断条故障

针对电机电流信号特征分析(motor current signature analysis,MCSA)诊断早期转子断条故障时存在的频谱泄露阻碍故障特征频率识别的问题,提出一种基于定子电流Morlet小波解调制信号分析的故障诊断方法。首先选择合适的参数对Morlet小波性能进行优化,继而利用优化后的Morlet小波提取鼠笼电机定子电流信号包络线以消除基频和噪声干扰的影响,然后对提取到的包络线作快速傅里叶变换(fast Fourier transform,FFT)分析,并根据FFT频谱中是否存在特征频率成分2sfs判断转子断条故障发生与否。所提方法在电机工频或变频供电方式、不同负载运行状况下都能够消除噪声干扰和频谱泄露影响,因而便于故障特征提取并实现早期转子断条故障诊断。理论分析和实验结果表明了所提方法的正确性和有效性。

基于多回路数学模型的异步电动机内部故障瞬变过程

基于多回路理论,建立了YN接、Y接、D接笼型异步电动机在正常、内部故障(定子绕组匝间短路与转子断条)情况下的多回路数学模型。应用多回路数学模型对电机内部故障瞬变过程进行数字仿真和实验,以此研究电机定子电流及其负序分量、相位对称关系、边频分量的瞬变规律。分析仿真与实验结果表明:由于实际电机本身固有的不对称性,即使其正常运行,也必将在一定程度上体现内部故障特征;某些形式的定子绕组匝间短路故障可能抵消这种不对称性,并削弱(而非强化)定子绕组匝间短路故障特征;实际电机定子电流往往包含包括转子断条故障特征分量在内的多个边频分量,其中某些边频分量可能来源于电机气隙偏心、转子不对中或其他因素。在工程实际中进行电机内部故障检测时,必须密切关注上述规律,以保证故障检测灵敏度与可靠性。

基于随机共振理论的异步电动机转子断条检测新方法

现场采样获得的电动机故障信号通常会含有很多噪声。传统的方法是对故障信号消噪后进行分析,但在消噪的同时会丢失一些有用信息。本文提出了基于随机共振(SR)理论的异步电动机转子断条早期故障检测新方法。该方法是通过选择合适的系统参数,使非线性系统发生随机共振,把一部分噪声能量转化成信号能量,达到异步电动机转子断条早期故障检测的目的。实验分析结果表明该方法是切实可行的。