变频器供电下异步电动机转子断条故障仿真研究

分析了变频器供电时所产生的谐波对电动机转子断条故障电流信号的影响,基于Matlab／Simulink建立了直接转矩控制变频器供电下异步电动机转子故障系统仿真模型,在变频器不同供电频率下针对电动机转子正常和故障情况进行仿真分析。理论上难了变频器谐波影响下电动机转子故障特征频率的正确性,利用连续细化傅立叶变换和自适应滤波方法实现了变频器不同频率供电下民步电动机转子故障的在线检测。     

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。     

基于小波包变换的牵引电机转子断条隐患特征提取方法研究

为了实现城轨列车牵引电机在运行时故障隐患的实时监控,根据牵引电机实际运行情况,在Matlab／Simulink仿真环境中建立起了处在闭环控制系统中的带有转子断条隐患的异步电机模型。对定子侧三相电流进行小波包分解并对各频段信号进行小波包系数重构,计算各频段的能量特征值,最后构建出隐患特征向量对电机转子隐患做出诊断。通过对仿真数据的应用,利用上述方法可以有效地识别出电机的转子断条隐患,为研制地铁列车牵引电机隐患挖掘与评估预警系统提供了技术参考。     

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。     

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。     

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。     

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DSP和LabVIEW的电动机转子断条故障诊断

为了实现对鼠笼式异步电动机转子断条故障的实时诊断,设计了一套以TMS320F2812和LabVIEW为核心的转子断条故障诊断装置。该装置以TMS320F2812为主控芯片,实现对异步电动机定子侧电流信号的采集,通过DSP的串口模块将采集到的信号传送到由LabVIEW构建的上位机信号处理平台。针对定子侧电流信号中工频分量对断条故障特征分量的干扰较大,严重地影响断条分量的识别,LabVIEW处理平台采用自适应陷波器算法对工频信号进行陷波处理,在FFT频谱上实现对断条故障特征分量的识别。实验证明,该装置能够实现对断条故障进行实时监测,并能够在FFT频谱上对断条故障特征分量进行识别。     

基于转子实验台的典型故障信号模拟与分析

介绍了一种通过转子实验台模拟转子不平衡和摩擦两种故障来获取该典型故障信号的方法,并对信号的产生进行了机理分析,给出了典型信号在正常和故障两种不同情况下的时域和频域分析图。将实验结果与理论分析相结合,验证了旋转机械两种典型振动故障的识别特征,丰富了故障档案。     

Motor square current signature analysis for induction motor rotor diagnosis

Induction machines play an important role in today’s industry. Thus, preventive maintenance combined with fault diagnosis techniques have become an essential issue. One of the most used techniques for the diagnosis of faults in the induction machine is motor current signature analysis (MCSA). This approach presents some limitations for induction motor rotor diagnosis, particularly for small faults. In this paper, a new motor square current signature analysis (MSCSA) fault diagnosis methodology is presented. The proposed technique is based on three main steps: first, the induction motor current is measured; secondly, the square of the current is computed; and finally a frequency analysis of the square current is performed. This technique allows more information to be obtained from a motor with a rotor fault than the classical MCSA approach. Simulation and experimental results are presented in order to confirm the theoretical assumptions. This methodology has also been tested for the identification of two distinct faults (broken bars and rotor eccentricity).     

Fast Fourier and discrete wavelet transforms applied to sensorless vector control induction motor for rotor bar faults diagnosis

This paper presents new techniques to evaluate faults in case of broken rotor bars of induction motors. Procedures are applied with closed-loop control. Electrical and mechanical variables are treated using fast Fourier transform (FFT), and discrete wavelet transform (DWT) at start-up and steady state. The wavelet transform has proven to be an excellent mathematical tool for the detection of the faults particularly broken rotor bars type. As a performance, DWT can provide a local representation of the non-stationary current signals for the healthy machine and with fault. For sensorless control, a Luenberger observer is applied; the estimation rotor speed is analyzed; the effect of the faults in the speed pulsation is compensated; a quadratic current appears and used for fault detection.     

Induction machine condition monitoring using notch-filtered motor current

This paper presents a new approach to induction motor condition monitoring using notch-filtered motor current signature analysis (NFMCSA). Unlike most of the previous work utilizing motor current signature analysis (MCSA) using spectral methods to extract required features for detecting motor fault conditions, here NFMCSA is performed in time-domain to extract features of energy, sample extrema, and third and fourth cumulants evaluated from data within sliding time window. Six identical three-phase induction motors were used for the experimental verification of the proposed method. One healthy machine was used as a reference, while other five with different synthetic faults were used for condition detection and classification. Extracted features obtained from NFMCSA of all motors were employed in three different and popular classifiers. The proposed motor current analysis and the performance of the features used for fault detection and classification are examined at various motor load levels and it is shown that a successful induction motor condition monitoring system is developed. Developed system is also able to indicate the load level and the type of a fault in multi-dimensional feature space representation. In order to test the generality and applicability of the developed method to other induction motors, data acquired from another healthy induction motor with different number of poles and rated power is also incorporated into the system. In spite of the above difference, the proposed feature set successfully locates the healthy motor within the classification cluster of “healthy motors” on the feature space.     

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.     

大型电动机的状态监测及诊断

电动机故障包括绝缘故障、定子故障、转子故障、轴承故障等。各种故障都会以一定的故障信号方式表现出来，而通过对信号中故障特征信号的提取分析可以对电动机故障进行判断。本文对电动机的多种基于信号监测的故障分析方法进行了原理分析，包括对定子电流信号的多种分析、轴承振动的频谱分析、电动机转速的波动分析等，对其他的多种故障监测方法也进行了介绍，并对每种分析方法所适用的故障诊断类型及优缺点给予了说明，最后指出了今后的发展趋势，为电动机故障诊断方法的应用提供了参考依据。     

DWT analysis of numerical and experimental data for the diagnosis of dynamic eccentricities in induction motors

The behaviour of an induction machine during a startup transient can provide useful information for the diagnosis of electromechanical faults. During this process, the machine works under high stresses and the effects of the faults may also be larger than those in steady-state. These facts may help to amplify the magnitude of the indicators of some incipient faults. In addition, fault components with frequencies dependant on the slip evolve in a particular way during that transient, a fact that allows the diagnosis of the corresponding fault and the discrimination between different faults. The discrete wavelet transform (DWT) is an ideal tool for analysing signals with frequency spectrum variable in time. Some research works have applied with success the DWT to the stator startup current in order to diagnose the presence of broken rotor bars in induction machines. However, few works have used this technique for the study of other common faults, such as eccentricities. In this work, time–frequency analysis of the stator startup current is carried out in order to detect the presence of dynamic eccentricities in an induction motor. For this purpose, the DWT is applied and wavelet signals at different levels are studied. Data are obtained from simulations, using a finite element (FE) model of an induction motor, which allows forcing several kinds of faults in the machine, and also from experimental tests. The results show the validity of the approach for detecting the fault and discriminating with respect to other failures, presenting for certain applications (or working conditions) some advantages over the traditional stationary analysis.     

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.     

Estimation of frequency components in stator current for the detection of broken rotor bars in induction machines

The limitation of data window length in induction machine broken rotor bar diagnostics is a real challenge in practice. Sideband frequencies which are used as broken rotor bar indicators are very close to the fundamental frequency and have low magnitude. Traditional spectral analysis approach such as Discrete Fourier Transform (DFT) can be inaccurate in these conditions due to its inherent drawbacks such as the requirement of long data window for high resolution and the side lobe leakage in frequency domain. In this paper, a high-resolution spectral analysis technique, Prony Analysis (PA), is proposed for broken rotor bar detection in induction machines. The method is described and demonstrated in detail, validated by experimental data, and compared with DFT. Results clearly indicate the advantages of PA over DFT in terms of maintaining a high resolution with a much shorter window and a better frequency estimate accuracy with the same window length.     

Symmetrical component filter based on line condition monitoring instrumentation system for mine winder motor

As the capital investment in underground coal mining is huge enough by the standards of any conventional industry hence coal production process has to be very efficient to make commercially viable. In a situation of intensive and massive investment, the economics of production would primarily depend on machine utilization indicated by machine availability. Thus machine available time i.e. the time that a machine is available to do productive work, has to be maximized, for best returns on capital invested and utilization of manpower.In this present research work an online condition monitoring instrumentation system has been developed for condition monitoring of mine winder motor. The instrumentation system has been developed based on current monitoring technique. The symmetrical current component present in the unbalanced motor current is sensed with the help of current transformer, current to voltage converter, all pass filters and adders. Any electrical fault in mine winder motor will produce unbalancing in the motor circuit and will cause for the development of symmetrical current component. The type of electrical fault can be determined by sensing the symmetrical current component. One important advantage of this condition monitoring technique is that the instrument can be made hand held and the same hand held instrument may be used for the fault diagnosis of other motors also.A novel condition monitoring instrumentation system based on symmetrical component filter has been developed for on-line condition monitoring of mine winder motor. The instrumentation system would be able to diagnose various incipient faults of mine winder motor and will increase the safety as well as availability of mine winder.The result obtained from symmetrical current component filter based motor diagnostic technique has been verified with the result obtained by axial leakage flux based motor diagnostic technique for similar simulated motor fault condition to pinpoint the exact faulty of condition of the model mine winder motor.     

Analysis of condition monitoring methods for electromotor on oil platform

Electromotor on oil platform often has mechanical failures.In order to achieve monitoring and diagnosis of the electromotor,common diagnostic methods of electromotor are summarized first,and then vibration monitoring is regarded as a suitable method for monitoring and diagnosing of mechanical failures by comparing the advantages and disadvantages of each method and characteristics of mechanical faults.At last,the fault frequencies and arrangements of vibration measuring points are analyzed.By using vibration monitoring method,the diagnosis of bearing faults of electromotor is carried out.The results show that the analysis of condition monitoring methods for electromotor is meanning for machine maintenance and repair,and it lays foundation for computerized repair system and resource management system.     

Coolant system health monitoring and fault diagnosis via health parameters and fault dictionary

A new methodology is described for the condition monitoring and fault diagnosis of machine tool coolant systems. The steady state characteristics of the coolant system pump outlet pressure, pump motor temperature and tank level are used to define health parameters from which the system health is deduced. On detection of a significant change in system health the pressure transient on closure of the flow valve is captured to aid diagnosis of the fault. A demonstration system for the coolant system of a Wadkin V4-6 machine tool is described. This is used to verify the usefulness of the health system described above by simulation of a number of typical faults.