异步电机转子故障诊断算法研究

针对异步电机定子电流信号频谱分析法对转子故障诊断时,转子断条和偏心故障特征分量容易受到基波分量的影响,难以准确诊断故障的情况,对传统的瞬时功率信号频谱分析法进行改进.利用Hilbert变换对定子电压、电流进行数学变换,在此基础上得到改进的瞬时功率,然后对改进后的瞬时功率信号进行频谱分析.通过搭建异步电机故障检测实验平台进行了初步模拟实验,实验结果表明,该方法不仅消除了基波分量对故障特征分量的影响,而且还使频谱曲线更加清晰、简洁,突显了故障特征信息,弱化了非故障特征分量,为提高异步电机转子断条和偏心故障诊断的准确性奠定了基础.     

Diagnosis of broken rotor bars in induction motors based on harmonic analysis of fault components using modified adaptive notch filter and discrete wavelet transform

This paper proposes a method for fast and accurate detection of broken rotor bars (BRBs) in a three-phase squirrel cage induction motor. The fundamental component of the stator current signal is extracted using a linear time-invariant filter. The resultant residual signal which contains the harmonic components of the current is then used to detect the BRBs, by means of discrete wavelet transform (DWT). Since in experiment it is not possible to break the rotor bars while the motor is under load, finite element method and MATLAB/Simulink are employed to accurately demonstrate the behavior of the running machine as the BRB happens. To get more accuracy, differential evolution (DE) optimization algorithm is used to obtain the corresponding fault impedance for the rotor external circuit of the MATLAB model. Detail coefficients (DCs) of the wavelet decomposition are employed as the new fault indicators. Simulation results show that using DCs of the harmonic component signal rather than the actual current signal, leads to more distinctive fault signatures in the wavelet decomposition. The obtained results suggest that the proposed fault detection scheme can be employed as a highly reliable technique for diagnosing rotor bar failures in running machines.     

基于小波和神经网络的异步电机转子故障诊断方法研究

基于小波包变换的频率划分特性．对定子电流的Park矢量模信号进行小波包分解，建立了转子断条的故障特征矢量，准确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，结合BP神经网络非线性映射及分类识别的优点，将BP神经网络应用于电机转子断务故障的识别，实验结果表明，该方法可实现转子断条故障的可靠诊断。     

Automatic detection and classification of rotor cage faults in squirrel cage induction motor

The detection of broken rotor bars and broken end-ring in three-phase squirrel cage induction motors by means of improved decision structure. The structure consists of current signal analysis (CSA), Artificial Neural Network (ANN) and diagnosis algorithm. Effects of broken bars and end-ring on current signal and feature extraction are in the CSA. The rotor cage faults are classified by using ANN. And result matrixes of ANN are considered two different ways for diagnosis. Then the diagnoses are compared with each other. In this study six different rotor faults, which are one, two, three broken bars, bar with high resistance, broken end-ring and healthy rotor, are investigated. The effects of different rotor faults on current spectrum, in comparison with other fault conditions, are investigated by analyzing side-bands in current spectrum. To reduce bad effects of changing of distance between the side-band and main component on the detection and classification of the faults, the spectrum is achieved with low definition. Thus, the improved decision structure diagnoses faulted rotors with 100% accuracy and classified rotor faults 98.33% accuracy.     

A fault detection scheme based on minimum identified uncertainty bounds violation for broken rotor bars in induction motors

In this paper, a novel method for broken bars fault detection in the case of three-phase induction motors and under different payloads will be presented and experimentally evaluated. In the presented approach, the cases of a partially or full broken rotor bars are being also considered, caused by: (a) drilling 4 mm and 8 mm out of the 17 mm thickness of the same rotor bar and (b) fully drilled (17 mm) one, two and three broken bars. The proposed fault detection method is based on the Set Membership Identification (SMI) technique and a novel proposed minimum boundary violation fault detection scheme, applied on the identified motor's parameters. The system identification procedure is being carried out on the simplified equivalent model of the induction motor, during the steady-state operation (non-fault case), while at the same time the proposed scheme is able to calculate on-line the corresponding safety bounds for the identified variables, based on a priori knowledge of the measuring corrupting noise (worst case encountered). The efficiency, the robustness and the overall performance of the established fault detection scheme is being extensively evaluated in multiple experimental studies and under various time instances of faults and load conditions.     

Self-organizing map approach for classification of mechanical and rotor faults on induction motors

Two neural network-based schemes for fault diagnosis and identification on induction motors are presented in this paper. Fault identification is performed using self-organizing maps neural networks. The first scheme uses the information of the motor phase current for feeding the network, in order to perform the diagnosis of load unbalance and shaft misalignment faults. The network is trained using data generated through the simulation of a motor-load system model, which allows including the effects of load unbalance and shaft misalignment. The second scheme is based on the motor’s active and reactive instantaneous powers, in order to detect and diagnose faults whose characteristic frequencies are very close each other, such as broken rotor bars and oscillating loads. This network is trained using data obtained through the experimental measurements. Additional experimental data are later applied to both networks in order to validate the proposal. It is demonstrated that the proposed strategies are able to correctly identify, both unbalanced and misaligned load, as well as broken bars and low-frequency oscillating loads, thus avoiding the need for an expert to perform the task.     

基于SVM与D-S证据理论的异步电动机转子断条故障诊断方法

目前异步电动机转子断条故障诊断方法都是基于从定子电流中提取出特征频率来对转子状态作出诊断的方法,当异步电动机空载或轻载时,该特征频率易受基频泄露的影响而很难得到,同时该特征频率受转速波动影响很大,单纯根据该特征频率对转子状态作出判断缺乏准确性。针对上述问题,提出了一种运用SVM与D-S证据理论对异步电动机转子断条故障进行识别的诊断方法。该方法基于扩展Park法与FFT变换法,分别从定子电流信号和振动信号中提取转子断条故障的特征信息,利用SVM对异步电动机的状态进行模式识别,并将识别结果形成彼此独立的证据,而后根据D-S证据融合规则进行融合处理,从而实现对异步电动机转子断条故障的准确识别。实验结果表明,该方法可以对异步电动机转子断条故障作出准确判断。     

牵引电机转子断条微弱故障电气特性分析与故障注入

对牵引电机断条早期微弱故障进行电气特性分析有利于研究转子断条故障.首先从导条金属电阻值在疲劳演化过程中的变化规律出发,引入损伤因子,得到单根导条断裂严重程度与牵引电机相电阻间的关系;然后通过迭加原理,将导条故障时的牵引电机看成是正常电机在故障导条处迭加反向电流源,得到单根导条断裂时定子电流故障特征分量值;最后建立定子电...     

鼠笼式异步电机转子故障程度诊断方法

针对异步电机启动阶段转子断条故障检测问题,提出了一种基于同步压缩小波变换SWT的瞬态电流特征分析方法,通过时频分布图来判断转子是否存在故障。在此基础上,通过曲线拟合的方法给出了导条电阻与SWT系数能量谱之间的关系,进一步给出了确定故障预警值和报警值的方法。最后提出了一种判断故障程度的指标。采用Ansoft Maxwell软件建立笼型异步电机故障仿真模型,仿真实验结果表明,该方法能有效地诊断转子不对称故障并评估出故障程度。     

Offline and online fault detection and diagnosis of induction motors using a hybrid soft computing model

In this paper, a hybrid soft computing model comprising the Fuzzy Min-Max (FMM) neural network and the Classification and Regression Tree (CART) for motor fault detection and diagnosis is described. Specifically, the hybrid model, known as FMM-CART, is used to detect and classify fault conditions of induction motors in both offline and online environments. A series of experiments is conducted, whereby the Motor Current Signature Analysis (MCSA) method is applied to form a database containing stator current signatures under different motor conditions. The signal harmonics from the power spectral density (PSD) are extracted, and used as the discriminative input features for fault classification with FMM-CART. Three main induction motor conditions, viz. broken rotor bars, stator winding faults, and unbalanced supply, are used to evaluate the effectiveness of FMM-CART. The results indicate that FMM-CART is able to detect motor faults in the early stage, in order to avoid further damage to the induction motor as well as the overall machine or system that uses the motor in its operations.     

Simplified detection of broken rotor bars in induction motors controlled in field reference frame

In this paper, a simple method for additional on-line detection of broken rotor bars in a squirrel cage induction motor controlled in rotor field co-ordinates using existing hardware is presented. Based on a previously presented approach, an algorithm for on-line calculation of the variance of stator voltage reference, which depends on the number of broken bars, has been developed. Due to its simplicity, it could run in parallel with a standard control algorithm in field reference frame using contemporary fixed- and floating-point processors, thus requiring minimum processing time. The algorithm uses internal reference values of the stator voltage; therefore no additional dedicated measurements are needed. Results were obtained at different operating points on an induction motor with gradually damaged rotor. Comparison with commonly used diagnostic method confirms the validity of the approach.     

基于电感变化的永磁同步电机新型启动方法

通过分析内埋式永磁同步电机(IPMsM)磁饱和特性,建立永磁同步电机数学模型.根据在施加不通电压矢量时电流瞬间特行不同,采用无位置传感技术,提出了一种检测永磁同步电机在停止状态时转子位置的方法.在此基础上,通过设定直轴电流值为零、交轴电流为常数,设计了一种控制定子交直轴电流的新型永磁同步电机变频启动方法.最后,在以ST...     

An adaptive artificial immune system for fault classification

Fault diagnosis is very important in ensuring safe and reliable operation in manufacturing systems. This paper presents an adaptive artificial immune classification approach for diagnosis of induction motor faults. The proposed algorithm uses memory cells tuned using the magnitude of the standard deviation obtained with average affinity variation in each generation. The algorithm consists of three steps. First, three-phase induction motor currents are measured with three current sensors and transferred to a computer by means of a data acquisition board. Then feature patterns are obtained to identify the fault using current signals. Second, the fault related features are extracted from three-phase currents. Finally, an adaptive artificial immune system (AAIS) is applied to detect the broken rotor bar and stator faults. The proposed method was experimentally implemented on a 0.37?kW induction motor, and the experimental results show the applicability and effectiveness of the proposed method to the diagnosis of broken bar and stator faults in induction motors.     

Sensorless fault tolerant control for induction motors

In this paper, a sensorless fault tolerant controller for induction motors is developed. In the proposed approach, a robust controller based on backstepping strategy is designed in order to compensate for both the load torque disturbance and the rotor resistance variation caused by the broken rotor bars faults. The proposed approach needs neither fault detection and isolation schemes nor controller re-design. Moreover, to avoid the use of speed and flux sensors, a second order sliding mode observer is introduced to estimate the flux and the speed. The observer converges in a finite time and leads to good estimates of the flux and the speed even in the presence of the rotor resistance variation and the load torque disturbance. Since the observer converges in the finite time, the stability of the closed-loop system (controller with observer) is shown in two steps. First, the boundedness of the closed-loop system trajectories before the convergence of the observer is proved. Second, the convergence of the closed-loop system trajectories is proved after the convergence of the observer. To highlight the efficiency and applicability of the proposed control scheme, simulation and experimental results are conducted for a 1.5 kW induction motor.     

Weighted preliminary-summation-based principal component analysis for non-Gaussian processes

For industrial chemical process, preliminary-summation-based principal component analysis (PS-PCA), an amended PCA method was recently provided for coping with both Gaussian and non-Gaussian characteristics. By summing the training and monitoring data respectively, PS-PCA is capable of resolving the issue of non-Gaussian processes and achieves higher fault detection rate than the traditional PCA. However, in the PS-PCA summation operation, all data samples are regarded as the same weight, which results in the fault information of newly-samples may be diluted, leading to significant detection delays. To address this challenge, in this paper, we propose a novel weighted PS-PCA (WPS-PCA) method that employs an exponential weighting scheme to put more emphasis on recent information. Subsequently, a mathematical argument demonstrates that when the number of variables is enough plentiful, the obtained summation combined with the generalized central limit theorem conforms to approximately a Gaussian distribution. The kurtosis relationships indicate this conversion will bring out well-pleasing feasibility for conventional PCA. Ultimately, the proposed technique verifies detection performance using the Tennessee Eastman process, which is compared with the existing PCA and PS-PCA schemes, in terms of the fault detection time and fault detection rate. The simulation studies reveal that the proposed method is efficient and superior.     

基于电流分析法的电动机故障诊断虚拟仪器系统的研制

运用电流分析法开发了电动机故障诊断虚拟仪器系统;分析了定子匝间短路、转子断条和气隙偏心三类典型的电动机故障,并得出了定子匝间短路的故障特征是定子电流中出现负序分量的结论;转子断条故障会在定子绕组中感应出频率为(1-2s)f的电流,而气隙偏心时的典型特征则是定子电流中出现(Rfr±f)频率成分;以此结论作为故障诊断的理论基础,在LabVIEW软件平台上开发了故障诊断的虚拟仪器系统,编程实现电动机的故障设别;LabVIEW搭建的界面友好,实时显示程序运行的各项结果,操作简单及方便;还对该虚拟仪器系统进行了硬件仿真实验,实验的结论表明设计系统是可行且有效的,能够应用于电动机的故障诊断。     

基于RVMD的牵引电机转子初期断条故障监测

针对牵引电机转子初期断条故障监测难的问题,提出一种基于重构变分模态分解(RVMD)的故障监测方法.该方法针对监测信号构造变分问题,求解多个模态函数,通过对模态函数进行叠加重构实现故障监测.结合损伤因子概念对电机转子初期断条故障进行建模,利用所建故障模型实现牵引电机转子初期故障注入,并进行故障监测实验.最后通过实验验证所提出方法的有效性.     

An application to transient current signal based induction motor fault diagnosis of Fourier–Bessel expansion and simplified fuzzy ARTMAP

The start-up transient signals have been widely used for fault diagnosis of induction motor because they can reveal early defects in the development process, which are not easily detected with the signals in the steady state operation. However, transient signals are non-linear and contain multi components which need a suitable technique to process and identify the fault pattern. In this paper, the fault diagnosis problem of induction motor is conducted by a data driven framework where the Fourier–Bessel (FB) expansion is used as a tool to decompose transient current signal into series of single components. For each component, the statistical features in the time and the frequency domains are extracted to represent the characteristics of motor condition. The high dimensionality of the feature set is solved by generalized discriminant analysis (GDA) implementation to decrease the computational complexity of classification. In the meantime, with the aid of GDA, the separation of the feature clusters is increased, which enables the more classification accuracy to be achieved. Finally, the reduced dimensional features are used for classifier to perform the fault diagnosis results. The classifier used in this framework is the simplified fuzzy ARTMAP (SFAM) which belongs to a special class of neural networks (NNs) and provides a lower training time in comparison to other traditional NNs. The proposed framework is validated with transient current signals from an induction motor under different conditions including bowed rotor, broken rotor bar, eccentricity, faulty bearing, mass unbalance and phase unbalance. Additionally, this paper provides the comparative performance of (i) SFAM and support vector machine (SVM), (ii) SVM in the framework and SVM combined with wavelet transform in previous studies, (iii) the use of FB decomposition and Hilbert transform decomposition. The results show that the proposed diagnosis framework is capable of significantly improving the classification accuracy.     

An efficient,simplified multiple-coupled circuit model of the induction motor aimed to simulate different types of stator faults

This paper proposes an original simplified model aimed to simulate, an easy way, inter turns short circuit fault, phase to phase fault and phase to ground fault. In this model, the stator is considered as six magnetically coupled windings and the rotor as three not magnetically coupled R–L circuits. The paper also presents the star- and delta-connected stator configurations of the simplified model. However, the proposed simplified model is suitable only for steady-state operation. The performance of the simplified model is first verified by a comparison between the simulated current of the multiple-coupled model and the simplified model. Then, since the stator faults have an impact on the symmetrical components of the stator current, this paper uses these components to validate the behavior of the simplified model by simulation and experimentally using a 1.1 kW motor. In addition, simulated results of the simplified model for a 110 kW motor are presented in order to generalize the use of the proposed model to larger motors.     

Support vector machine for fault diagnosis of the broken rotor bars of squirrel-cage induction motor

The paper presents an automatic computerized system for the diagnosis of the rotor bars of the induction electrical motor by applying the support vector machine. Two solutions of diagnostic system have been elaborated. The first one, called fault detection, discovers only the case of the fault occurrence. The second one (complex diagnosis) is able to find which bars have been damaged. The most important problem is concerned with the generation and selection of the diagnostic features, on the basis of which the recognition of the state of the rotor bars is done. In our approach, we use the spectral information of the motor current, voltage and shaft field of one phase registered in an instantaneous form. The selected features form the input vector applied to the support vector machine, used as the classifier. The results of the numerical experiments are presented and discussed in the paper.