变频供电异步电动机转子断条故障诊断

异步电机转子断条故障发生时,定子电流（变频器输出侧电流）中会出现对称频率（1±2s）f1（f1为定子电流频率）的故障特征附加电流信号。以此为依据,定子电流特征频谱分析（MCSA）发展为经典转子断条故障在线检测方法。在工程实际过程中,变频供电异步电动机容易采集到的信号是开关柜二次侧供电电流（变频器输入侧电流）．因此要实现变频异步电动机转子断条故障诊断,必须清楚供电电流中是否也含有断条故障特征信息。首次对变频异步电动机供电电流进行分析．得出供电电流中也包括转子断条故障特征信息的结论,以此为基础。利用连续细化傅立叶和自适应滤波相结合的方法,实现了变频异步电动机转子断条故障诊断。     

电流特征分析在异步电动机故障诊断中的应用

在线运行条件下,采集笼型异步电动机的定子电压和电流作为检测电动机故障的状态参数,并对电压和电流信号进行频谱分析,可以准确地检测出包括定子转子断条、绕组匝间短路、轴承磨损等各种常见的故障类型。     

异步电动机故障诊断的小波变换等高图方法

用Morlet小波作为小波基，对异步电动机鼠笼转子故障时的定子电流信号进行多尺度分析，将获得的小波变换系数用等高图表示，从中能清楚地识别出异步电机鼠笼转子不同断条的故障。较基于傅立叶变换的故障诊断，该方法对异步电动机故障的辩识能力有显著提高。     

转角不对中转子系统的电机电流特性

为了通过监测电机电流以实现转子系统故障的识别与诊断,建立偏角不对中转子系统动力学模型,推导出转子系统的转角不对中力矩,并以电机的电磁扭矩为纽带,在MATLAB/Simulink环境下建立三相异步电动机—转子系统机电耦合仿真模型,应用傅里叶变换对定子电流信号进行频谱分析,研究偏角不对中故障激励下电机电流信号的耦合特性。仿真结果表明:正常状态下,电流信号会产生工频分量和奇数次倍频的谐波分量。当转子系统存在转角不对中故障时,三次谐波分量会出现明显的增强,而且其峰值会随着转角不对中量的增加而变大。     

基于DSP的FFT算法实现

快速傅立叶变换(FFT)是将信号从时域变换到频域的一种方法,广泛运用于各种信号分析领域。文中介绍了FFT算法的原理,构建了基于TMS320F2812的硬件平台,阐述了FFT算法的硬件与软件实现。利用TMS320F2812内部的ADC模块与事件管理器的定时器实现信号的实时采集,不需要使用专门的A/D转换芯片。软件上以128点FFT运算为例,在CCS环境下利用C语言编程实现了FFT算法,程序充分利用蝶式权的周期性及FFT运算中第一级蝶式权值固定为1的特点,使得运算量与复杂度大大减小。运行结果表明TMS320F2812能够快速高效地完成FFT运算。     

电机故障诊断支持向量机

基于数据的机器学习是现代智能技术中的重要方面。统计学习理论(Statistical learnmgtheory SLT)是研究小样本情况下机器学习规律的新理论。支持向量机(Support vector machine SVM)是在这一理论体系基础上发展起来的一种通用学习方法。SLT和SVM正成为继神经网络研究之后新的研究热点。通过对鼠笼式异步电动机转子断条故障进行实验模拟，对实验获取的采样电流信号经FFT分析，构造以低频到高频的频谱特性为分量的学习样本向量，通过支持向量机SVM对故障电流样本的训练，使SVM具有分类功能。最后，采用SVM对电动机各种转子断条故障进行诊断分类，取得较满意的结果，说明支持向量机SVM是进行故障诊断的一种新方法。     

基于旋转不变信号参数估计技术与模式搜索算法的异步电动机转子故障检测新方法

提出一种基于旋转不变信号参数估计技术(Estimation of signal parameters via rotational invariance technique,ESPRIT)与模式搜索算法(Pattern search algorithm,PSA)的异步电动机转子故障检测新方法。模拟形成转子故障情况下的定子电流信号并以之检验ESPRIT性能。结果表明:即使对于短时信号,ESPRIT仍具备高频率分辨力,可以准确估计定子电流各个分量的频率;但对其幅值、初相角的估计欠缺准确性、稳定性。随后,采用PSA确定各个频率分量的幅值、初相角。对一台异步电动机完成了转子故障检测试验,结果表明:基于ESPRIT与PSA的异步电动机转子故障检测方法是切实可行的,并且因仅需短时信号即可达到高频率分辨力而适用于负荷波动情况。     

基于小波包分析的电机转子故障诊断方法研究

针对异步电动机转子断条故障突发率高的原因,开展了基于小波包分析的故障诊断方法研究。通过小波包灵活的时频分析方法捕捉到的特征信号来确定故障信号的突变点和频谱特性,找出其故障特征,该方法为电机转子断条故障提供了一种有效的诊断方法。     

模糊小波神经网络在电机断条故障检测的研究

鼠笼式异步电机的转子绕组由导条和端环组成,最常见的故障是断条和端环断裂.诱发原因主要有:设计制造不合理、频繁起停、交变负载使导条和端环因冷热循环而疲劳断裂、电压波动造成电流过大而过热等.针对这些故障,文中用模糊小波神经网络的分析方法,有效地提取了转子断条故障的特征信息,克服了传统基于FFT分析方法难以提取故障特征频率分量的难点,并依靠神经网络诊断方法,准确地识别出了电动机转子断条故障.     

平行不对中转子系统的电机电流特性

为通过监测电机电流信号以实现对转子系统故障的识别与诊断,考虑圆盘不平衡因素,建立平行不对中转子系统动力学模型,然后应用拉格朗日方程,推导出转子系统运动微分方程,并以电机的电磁扭矩为纽带,在MATLAB/Simulink环境下建立三相异步电动机—转子系统机电耦合模型,最后应用傅里叶变换对电流信号进行频谱分析,研究平行不对中故障激励下电机电流信号的耦合特性。仿真结果表明:不平行故障会使电流信号激发出的边频分量,随着不平衡量的增大,还会激发出的边频分量;考虑质量偏心,电流信号还会激发出的边频分量,但当时,的边频分量会被淹没,反之,该边频分量则比较明显。     

基于高分辨率谱估计的早期转子断条故障诊断

以快速傅里叶变换(FFT)为基础的电机电流信号特征分析(MCSA)具有频率分辨率低的固有缺陷,从而严重影响了鼠笼电机早期转子断条故障的诊断性能。为解决这一问题,提出基于高分辨率谱估计的早期转子断条故障诊断方法。首先利用Hilbert变换和离散小波变换对单相定子电流信号预处理,然后采用扩展Prony算法对预处理后的信号进行定性/定量分析。运用该方法对不同故障严重程度、不同负载条件下的3 k W电机稳态定子电流信号进行分析,并与FFT分析结果做对比。实验结果表明,即使在短时数据条件下所提方法仍然能够准确诊断出早期转子断条故障,验证了该方法的有效性和优越性。     

鼠笼电机转子断条故障的定子电流信号平方解调分析诊断方法

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。     

电机断条故障下液压动力源特性仿真研究

以异步电机和齿轮泵组成的液压动力源为研究对象,深入分析了电机转子断条故障引起的电磁转矩、转速和功率产生脉动的机理。利用多回路法建立了电机转子断条故障下液压动力源的动态数学模型,并对其主要机、电、液参数进行仿真分析,结果表明,电机发生断条故障时,由于定子电流边频分量与转子系统的耦合作用,使得液压动力源的输入输出参量均出现不同程度的脉动,脉动程度与频率均随故障程度的加剧及负载的增大而增大,影响液压动力源的平稳运行;同时证明,电机发生转子断条故障时,液压动力源的效率亦大幅下降。     

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 estimation 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 frequencies of the fundamental and fault characteristic components 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.     

基于MCSA和SVM的异步电机转子故障诊断

本文提出一种基于电机电流信号频谱分析和支持向量机的异步电机转子故障诊断方法，该方法可以利用支持向量机对电机电流频谱信号的特征信息和故障模式进行关联。对电机定子电流采样后，其信号经FFT变换后提取故障特征量作为支持向量机的输入，基于1对1算法构造了感应电机转子故障多类分类器。实验结果表明，该方法具有很好的分类和泛化能力，可以提高电机故障诊断的准确性。     

交流电机转子断条故障程度诊断方法的研究

转子断条故障程度检测包括转子断条数目检测和断条时间的检测。M ATLAB仿真证明,对重构后的特征能量系数做快速傅里叶变换可以得到特征频率值和故障特征量的幅值,由此可推断出转子断条故障程度的相关信息,为故障程度判断提供依据。     

A new diagnosis of broken rotor bar fault extent in three phase squirrel cage induction motor

This paper proposes a new induction motor broken bar fault extent diagnostic approach under varying load conditions based on wavelet coefficients of stator current in a specific frequency band. In this paper, winding function approach (WFA) is used to develop a mathematical model to provide indication references for parameters under different load levels and different fault cases. It is shown that rise of number of broken bars and load levels increases amplitude of the particular side band components of the stator currents in faulty case. Stator current, rotor speed and torque are used to demonstrate the relationship between these parameters and broken rotor bar severity. An induction motor with 1, 2 and 3 broken bars and the motor with 3 broken bars in experiment at no-load, 50% and 100% load are investigated. A novel criterion is then developed to assess rotor fault severity based on the stator current and the rotor speed. Simulations and experimental results confirm the validity of the proposed approach.     

A validated model for the prediction of rotor bar failure in squirrel-cage motors using instantaneous angular speed

Instantaneous angular speed (IAS)-based condition monitoring is an area in which significant progress has been achieved over the recent years. This condition monitoring technique is less known compared to the existing conventional methods. This paper presents model-predicted simulation and experimental results of broken rotor bar faults in a three-phase induction motor using IAS variations. The simulation was performed under normal, and a broken rotor bar fault. The present paper evaluates through simulating and measuring the IAS of an induction motor at broken rotor bar faults in both time and frequency domains. Experimental results show a good agreement with the model-predicted simulation results. Three vital key features were extracted from the angular speed variations. One feature is the modulating contour of pole pass frequency periods in time domain. The other two features are in frequency domain. The primary feature is the presence of the pole pass frequency component at the low-frequency region of the IAS spectrum. The secondary feature which are the multiple of pole pass frequency sideband components around the rotor speed frequency component. Experimental results confirm the validity of the simulation results for the proposed method. The IAS has demonstrated more sensitivity than current signature analysis in detecting the fault. This research also shows the power of angular speed features as a useful tool to detect broken rotor bar deteriorations using any economical transducer such as low-resolution rotary shaft encoders; which may well be already installed for process control purposes.     

Novel approach using Hilbert Transform for multiple broken rotor bars fault location detection for three phase induction motor

This paper presents a new approach to detect the location of multiple broken rotor bars (MBRBs) in induction motor (IM) drive, running under no load and full load conditions using direct in and variable frequency drives. This technique is based on earlier work of location detection of one broken rotor bar. The techniques are tested for various fault severity levels so the detection of the exact location of the fault at early stage helps to reach sufficient time maintenance. In this paper, the authors used Hilbert Transform to extract the fault signature from the stator current envelope which is the low frequency component. Then statistical analysis is applied which produce a formula that is used to get the exact location of the fault in IM rotor.