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.     

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.     

Identifying three-phase induction motor faults using artificial neural networks

This paper presents an artificial neural network (ANN) based technique to identify faults in a three-phase induction motor. The main types of faults considered are overload, single phasing, unbalanced supply voltage, locked rotor, ground fault, over-voltage and under-voltage. Three-phase currents and voltages from the induction motor are used in the proposed approach. A feedforward layered neural network structure is used. The network is trained using the backpropagation algorithm. The trained network is tested with simulated fault current and voltage data. Fault detection is attempted in the no fault to fault transition period. Off-line testing results on a 3 HP induction motor model show that the proposed ANN based method is effective in identifying various types of faults.     

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。     

Quantitative analysis of noninvasive diagnostic procedures for induction motor drives

This paper reports quantitative analyses of spectral fault components in five noninvasive diagnostic procedures that use input electric signals to detect different types of abnormalities in induction motors. Besides the traditional one phase current spectrum analysis “SC”, the diagnostic procedures based on spectrum analysis of the instantaneous partial powers “P_ab”, “P_cb”, total power “P_abc”, and the current space vector modulus “csvm” are considered. The aim of this comparison study is to improve the diagnosis tools for detection of electromechanical faults in electrical machines by using the best suitable diagnostic procedure knowing some motor and fault characteristics. Defining a severity factor as the increase in amplitude of the fault characteristic frequency, with respect to the healthy condition, enables us to study the sensitivity of the electrical diagnostic tools. As a result, it is shown that the relationship between the angular displacement of the current side-bands components at frequencies (f±f_osc) is directly related to the type of induction motor faults. It is also proved that the total instantaneous power diagnostic procedure was observed to exhibit the highest values of the detection criterion in case of mechanical faults while in case of electrical ones the most reliable diagnostic procedure is tightly related to the value of the motor power factor angle and the group motor-load inertia. Finally, simulation and experimental results show good agreement with the fault modeling theoretical results.     

Development of a low cost test rig for standalone WECS subject to electrical faults

In this paper, a contribution to the development of low-cost wind turbine (WT) test rig for stator fault diagnosis of wind turbine generator is proposed. The test rig is developed using a 2.5 kW, 1750 RPM DC motor coupled to a 1.5 kW, 1500 RPM self-excited induction generator interfaced with a WT mathematical model in LabVIEW. The performance of the test rig is benchmarked with already proven wind turbine test rigs. In order to detect the stator faults using non-stationary signals in self-excited induction generator, an online fault diagnostic technique of DWT-based multi-resolution analysis is proposed. It has been experimentally proven that for varying wind conditions wavelet decomposition allows good differentiation between faulty and healthy conditions leading to an effective diagnostic procedure for wind turbine condition monitoring.     

Fault diagnosis of motor drives using stator current signal analysis based on dynamic time warping

Electrical motor stator current signals have been widely used to monitor the condition of induction machines and their downstream mechanical equipment. The key technique used for current signal analysis is based on Fourier transform (FT) to extract weak fault sideband components from signals predominated with supply frequency component and its higher order harmonics. However, the FT based method has limitations such as spectral leakage and aliasing, leading to significant errors in estimating the sideband components. Therefore, this paper presents the use of dynamic time warping (DTW) to process the motor current signals for detecting and quantifying common faults in a downstream two-stage reciprocating compressor. DTW is a time domain based method and its algorithm is simple and easy to be embedded into real-time devices. In this study DTW is used to suppress the supply frequency component and highlight the sideband components based on the introduction of a reference signal which has the same frequency component as that of the supply power. Moreover, a sliding window is designed to process the raw signal using DTW frame by frame for effective calculation. Based on the proposed method, the stator current signals measured from the compressor induced with different common faults and under different loads are analysed for fault diagnosis. Results show that DTW based on residual signal analysis through the introduction of a reference signal allows the supply components to be suppressed well so that the fault related sideband components are highlighted for obtaining accurate fault detection and diagnosis results. In particular, the root mean square (RMS) values of the residual signal can indicate the differences between the healthy case and different faults under varying discharge pressures. It provides an effective and easy approach to the analysis of motor current signals for better fault diagnosis of the downstream mechanical equipment of motor drives in the time domain in comparison with conventional FT based methods.     

基于Park矢量的电动机轴承故障检测方法

提出了一种基于Park矢量的改进聚类处理算法,该方法通过辨识感应电动机三相定子电流中的故障信息来识别轴承故障。为了验证该方法的有效性,在电动机轴承上预设了故障,通过数字信号处理器采集数据,并利用上述方法进行处理。结果表明,所提出的方法可有效识别电动机的轴承故障。     

Electrical motor current signal analysis using a modified bispectrum for fault diagnosis of downstream mechanical equipment

This paper presents the use of the induction motor current to identify and quantify common faults within a two-stage reciprocating compressor based on bispectrum analysis. The theoretical basis is developed to understand the nonlinear characteristics of current signals when the motor undertakes a varying load under different faulty conditions. Although conventional bispectrum representation of current signal allows the inclusion of phase information and the elimination of Gaussian noise, it produces unstable results due to random phase variation of the sideband components in the current signal. A modified bispectrum based on the amplitude modulation feature of the current signal is then adopted to combine both lower sidebands and higher sidebands simultaneously and hence characterise the current signal more accurately. Based on this new bispectrum analysis a more effective diagnostic feature, namely normalised bispectral peak, is developed for fault classification. In association with the kurtosis value of the raw current signal, the bispectrum feature gives rise to reliable fault classification results. In particular, the low feature values can differentiate the belt looseness from the other fault cases and different degrees of discharge valve leakage and inter-cooler leakage can be separated easily using two linear classifiers. This work provides a novel approach to the analysis of stator current for the diagnosis of motor drive faults from downstream driving equipment.     

基于绕组函数的长定子直线同步电机建模研究

针对精确的直线同步电机数学模型建立困难的问题,提出了基于绕组函数法的长定子直线同步电机绕组电感的计算方法,并推导出了直线电机各电感参数计算公式,然后建立了相应的直线同步电机数学模型。此外,利用电感参数在Simulink里搭建了直线同步电机矩阵微分方程,通过仿真结果得到了三相电流、励磁电流和电磁推力波形。最后,基于长定子同步直线电机的结构尺寸建立了一对极有限元模型,定子绕组通入三相交流电,动子绕组通入直流励磁电流,通过设置适当的剖分,将得到的电磁推力和解析法的电磁推力进行了对比。研究结果表明,有限元分析法和解析法得到的电磁推力结果一致,从而论证了基于绕组函数法的直线电机数学模型建模方法,为以后的直线电机数学建模提供了参考。     

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.     

Application to induction motor faults diagnosis of the amplitude recovery method combined with FFT

This paper presents a signal processing method – amplitude recovery method (abbreviated to ARM) – that can be used as the signal pre-processing for fast Fourier transform (FFT) in order to analyze the spectrum of the other-order harmonics rather than the fundamental frequency in stator currents and diagnose subtle faults in induction motors. In this situation, the ARM functions as a filter that can filter out the component of the fundamental frequency from three phases of stator currents of the induction motor. The filtering result of the ARM can be provided to FFT to do further spectrum analysis. In this way, the amplitudes of other-order frequencies can be extracted and analyzed independently.If the FFT is used without the ARM pre-processing and the components of other-order frequencies, compared to the fundamental frequency, are fainter, the amplitudes of other-order frequencies are not able easily to extract out from stator currents. The reason is when the FFT is used direct to analyze the original signal, all the frequencies in the spectrum analysis of original stator current signal have the same weight. The ARM is capable of separating the other-order part in stator currents from the fundamental-order part.Compared to the existent digital filters, the ARM has the benefits, including its stop-band narrow enough just to stop the fundamental frequency, its simple operations of algebra and trigonometry without any integration, and its deduction direct from mathematics equations without any artificial adjustment.The ARM can be also used by itself as a coarse-grained diagnosis of faults in induction motors when they are working.These features can be applied to monitor and diagnose the subtle faults in induction motors to guard them from some damages when they are in operation. The diagnosis application of ARM combined with FFT is also displayed in this paper with the experimented induction motor. The test results verify the rationality and feasibility of the ARM.It should be clarified that the ARM must be applied in three phases of currents in electrical machines. For a single phase of alternating current or direct current, it can do nothing. However, since three-phase electrical machines have a dominant position in the application field in modern economic society and it is natural and convenient to acquire three phases of stator currents during the three-phase electrical machines are tested, it is necessary and meaningful to develop the ARM to diagnose and guard them.     

基于Maxwell的六相双Y移30°风力发电机的仿真分析

针对如何进一步降低永磁风力发电机的高次谐波含量,降低发电机铜耗和铁耗,以及有效地提高风力发电机性能的问题,在风力发电机设计研发中应用了六相双Y移30°绕组的多相绕组电机理论技术.利用Ansoft Maxwell 2D瞬态仿真功能,对采用相同定转子结构的普通三相60°相带发电机与六相双Y移30°绕组发电机,仅改变定子绕组的连接方式,分别进行了二维瞬态场建模.进行了空载运行和额定负载运行工况的瞬态有限元仿真分析,并针对负载时气隙磁密等进行了谐波的傅里叶分析.依据仿真分析中电流、电压、磁场等结果数据,进行了二者性能的区别比较.研究结果表明,六相双Y移30°发电机相比普通三相60°相带发电机而言,具有高次谐波含量低、电压范围大、电机损耗低、效率高等优势.     

发电机偏心故障的机电多重特征分析

详细分析了发电机发生气隙偏心故障时定子绕组并联支路的环流特征。首先推导了发电机正常运行时定子绕组并联支路间电压差的表达式,然后通过分析气隙静偏心和动偏心时各参数的变化得到了定子绕组并联支路的环流特性,从而得到了偏心故障的机电多重特征。实测的SDF-9型故障模拟发电机数据与理论分析结果基本吻合,揭示了气隙偏心的机械故障与发电机电气特征之间的关系,指出发电机的电流特征也可作为诊断发电机机械故障的依据。     

发电机绕组故障时振动的关联维数分析及诊断

运用分形理论的相空间重构算法,分析了发电机在正常运行、转子励磁绕组短路和定子绕组故障3种状态时定转子振动信号的关联维数。分析结果表明,3种状态具有不同的关联维数,揭示了绕组短路的电气故障不仅引起发电机电气参数的变化,也将引起发电机振动特征的变化,且不同绕组故障对气隙磁场畸变效果不同,引起发电机定转子不同的动力学特性。最后,以定子振动的关联维数作为特征向量输入到BP网络中,将实测M JF-30-6型故障模拟发电机的定子振动的关联维数作为样本,通过学习样本的学习和对测试样本的测试分析的结果表明,该诊断方法能正确识别发电机的3种状态。     

GENERATOR VIBRATION FAULT DIAGNOSIS METHOD BASED ON ROTOR VIBRATION AND STATOR WINDING PARALLEL BRANCHES CIRCULATING CURRENT CHARACTERISTICS

Rotor vibration characteristics are first analyzed,which are that the rotor vibration of fundamental frequency will increase due to rotor winding inter-turn short circuit fault,air-gap dynamic eccentricity fault,or imbalance fault,and the vibration of the second frequency will increase when the air-gap static eccentricity fault occurs.Next,the characteristics of the stator winding parallel branches circulating current are analyzed,which are that the second harmonics circulating current will increase when the rotor winding inter-turn short circuit fault occurs,and the fundamental circulating current will increase when the air-gap eccentricity fault occurs,neither being strongly affected by the imbalance fault.Considering the differences of the rotor vibration and circulating current characteristics caused by different rotor faults,a method of generator vibration fault diagnosis,based on rotor vibration and circulating current characteristics,is developed.Finally,the rotor vibration and circulating current of a type SDF-9 generator is measured in the laboratory to verify the theoretical analysis presented above.     

Intelligent fault monitoring and diagnosis in electrical machines

The aim of this paper is to develop an intelligent diagnosis method for fault detection and isolation in induction motors. We consider failures in three components of induction motor: bearing, stator winding and rotor winding. Firstly, a model-based nonlinear observer in the proposed method is designed based on available information. The fault detection decision is carried out by comparing the model-based observer speed with their signatures. Secondly, multiple state observers are constructed based on possible fault function set. The fault isolation decision is made by checking each residual generated by observer state estimation. Finally, simulation tests are given to verify the effectiveness of the proposed fault diagnosis scheme.     

Simulative and experimental investigation on stator winding turn and unbalanced supply voltage fault diagnosis in induction motors using Artificial Neural Networks

The three-phase shift between line current and phase voltage of induction motors can be used as an efficient fault indicator to detect and locate inter-turn stator short-circuit (ITSC) fault. However, unbalanced supply voltage is one of the contributing factors that inevitably affect stator currents and therefore the three-phase shift. Thus, it is necessary to propose a method that is able to identify whether the unbalance of three currents is caused by ITSC or supply voltage fault. This paper presents a feedforward multilayer-perceptron Neural Network (NN) trained by back propagation, based on monitoring negative sequence voltage and the three-phase shift. The data which are required for training and test NN are generated using simulated model of stator. The experimental results are presented to verify the superior accuracy of the proposed method.     

五相永磁同步电机一相断路容错策略推导及仿真验证

针对多相电机的开路故障时的容错运行控制策略及其验证问题,对五相永磁电机一相定子绕组开路的容错策略、五相电机Matlab仿真建模及容错策略的仿真验证进行了研究。根据定子绕组磁动势不变的原则,利用基本的三角函数变换,详细推导了五相永磁电机一相开路时的电流容错策略,利用旋转坐标系下的五相永磁电机数学模型,在Matlab中建立了电流源激励的五相永磁电机模型,对一相开路时的电流容错策略进行了仿真验证。研究结果表明,五相永磁电机开路故障时电流容错控制策略的推导方法和结论正确,在Matlab中建立电流源激励的五相永磁电机模型,是验证五相电机电流容错策略的简单、有效的方法。     

Analysis of field-oriented controlled induction motor drives under sensor faults and an overview of sensorless schemes

To obtain high dynamic performance on induction motor drives (IMD), variable voltage and variable frequency operation has to be performed by measuring speed of rotation and stator currents through sensors and fed back them to the controllers. When the sensors are undergone a fault, the stability of control system, may be designed for an industrial process, is disturbed. This paper studies the negative effects on a 12.5 hp induction motor drives when the field oriented control system is subjected to sensor faults. To illustrate the importance of this study mine hoist load diagram is considered as shaft load of the tested machine. The methods to recover the system from sensor faults are discussed. In addition, the various speed sensorless schemes are reviewed comprehensively.