异步电机转子电阻辩识方法的建模与仿真

研究异步电机稳定性优化控制问题,异步电机转子电阻受温度变化、集肤效应等影响,具有时变性和非线性,影响系统稳定性分析。传统方法对异步电机转子电阻状态辩识准确率低。为了准确和快速对转子电阻进行状态辩识,提出一种扩展卡尔曼滤波的异步电机转子电阻辩识方法。将转子电阻看成系统状态变量,设计一种扩展卡尔曼滤波技术的转子电阻估计器,通过测量电机定子电压、电流,实现对转子电阻在线辩识。在MATLAB/Simulink中建立仿真模型,仿真结果表明,异步电机转子电阻改进辩识方法可提高转子电阻辨识准确率。     

基于BP网络的异步电机转子断条故障的自动识别方法

提出了一种以扩展Park矢量方法为故障特征提取手段、利用BP网络的模式识别功能自动诊断异步电机转子断条故障的新方法。该方法消除了故障信号中基频成分对断条故障特征分量的“湮没”影响,同时实现了故障的自动识别,免去了人为介入。故障诊断实例表明:该方法具有良好的有效性和准确性。     

加权模糊相对熵在电机转子故障模糊识别中的应用

提出了一种基于加权模糊相对熵的电机转子故障模糊识别方法．该方法将加权思想引入到模糊相对熵,用于识别电机转子故障严重程度．加权方法的引入增加了信息量丰富的符号区间的模糊相对熵占全部区间模糊相对熵的比重,可以更充分、合理地利用该区间的故障信息进行故障识别．电机转子断条故障诊断仿真实验结果表明,提出的方法有效地实现了电机故障的定量分析,能够准确地识别出电机转子故障的严重程度,使算法的鲁棒性得到了改善,故障分类的可靠性及准确程度得到了提高．     

异步电动机矢量控制调速系统的MATLAB/SIMULINK仿真

基于矢量控制理论,从异步电机数学模型入手,介绍了一种异步电机按转子磁场定向的矢量控制系统,用MATLAB/SIMULINK构建控制系统仿真模型,最后给出仿真结果,并给予相应的分析.     

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.     

无轴承异步电机的有限元分析及控制

无轴承异步电机是将旋转的转子悬浮于空间,使转子和定子之间没有接触的一种新型高性能电机。本课题在无轴承异步电机数学模型的基础上,通过Ansoft/Maxwell有限元分析软件对电机进行仿真分析,验证了新型无轴承异步电机的可行性。利用MATLAB/SIMULINK建立了基于自抗扰控制器的无轴承异步电机SVM-DTC控制系统,仿真结果表明该控制方法能抑制转速超调,对外部扰动有很强的抗干扰能力。     

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。     

基于SWT与等价空间的LDTV系统故障检测

为提高基于等价空间的线性离散时变（Linear discrete time-varying,LDTV）系统故障检测的检测性能,本文提出一种基于平稳小波变换（Stationary wavelet transform,SWT）与等价空间的LDTV系统故障检测方法.通过引入SWT对基于低阶等价关系构造的残差进行多尺度滤波,将残差产生器设计转化为不同尺度下的多目标最优化问题,保证了各尺度下残差对干扰鲁棒性和对故障灵敏性指标的最小化,同时利用SWT快速算法获得一组多尺度残差信号.进一步,对产生的多尺度残差信号进行多分辨率分析,从而实现较宽频率范围内故障信号的检测,有效降低了故障漏报率.最后,通过仿真实验验证了本文方法的有效性.     

异步电机定子电流的内模自适应控制及实现

提出了异步电机定子电流的内模自适应控制及其在转子磁场定向矢量控制中的实现方法。首先,根据内模控制（ＩＭＣ）原理设计异步电机电流调节器,并用矩阵奇异值分析了ＩＭＣ电流调节器的鲁棒性;然后用最小二乘法对模型参数进行辨识;最后将其应用于异步电机转子磁场定向的矢理控制中,通过对电流调节器传递矩阵函数的仿真及用ＤＳＰ实现的异步电机矢量控制运行实验,验证了自适应ＩＭＣ电流调节器的良好性能。     

电力电子电路故障的小波分形检测方法

根据电力电子装置故障产生的信号表现的奇异性,提出了一种基于小波分析的电力电子开路故障的小波分行检测方法。以三相桥式整流电路的开路故障为研究对象,将各类故障的电压输出波形进行多尺度分解,然后利用改进的关联维数计算方法计算不同故障状态下的关联维数,来刻画不同故障对应的不规则程度判断故障的类型,通过判断出现奇异性的时机判断具体器件的开路故障。最后给出故障诊断的算法,仿真结果表明了该方法的有效性。     

基于改进瞬时功率法的电动机故障诊断

从采集的鼠笼异步电动机定子电流出发,建立了流方的概念,通过故障电流的自乘方放大并转移故障特征频率。根据瞬时功率的概念提出了基于改进瞬时功率法的电动机故障诊断方法,通过理论推导分别提取了转子断条故障和转子偏心故障在流方中的特征频率分量,有效地克服了转子断条故障特征频率容易被基频淹没的缺点,实现了对转子断条、偏心、复合等故障的辨别诊断。该方法与传统瞬时功率法相比,采集的数据量减半,避免了电压波动和采样误差对瞬时功率的影响。     

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...     

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.     

Principal Component Analysis of the start-up transient and Hidden Markov Modeling for broken rotor bar fault diagnosis in asynchronous machines

This article presents a novel computational method for the diagnosis of broken rotor bars in three phase asynchronous machines. The proposed method is based on Principal Component Analysis (PCA) and is applied to the stator’s three phase start-up current. The fault detection is easier in the start-up transient because of the increased current in the rotor circuit, which amplifies the effects of the fault in the stator’s current independently of the motor’s load. In the proposed fault detection methodology, PCA is initially utilized to extract a characteristic component, which reflects the rotor asymmetry caused by the broken bars. This component can be subsequently processed using Hidden Markov Models (HMMs). Two schemes, a multiclass and a one-class approach are proposed. The efficiency of the novel proposed schemes is evaluated by multiple experimental test cases. The results obtained indicate that the suggested approaches based on the combination of PCA and HMMs, can be successfully utilized not only for identifying the presence of a broken bar but also for estimating the severity (number of broken bars) of the fault.     

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.     

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.     

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.     

Accurate diagnosis of induction machine faults using optimal time–frequency representations

This paper presents a new diagnosis method of induction motor faults based on time–frequency classification of the current waveforms. This method is composed of two sequential processes: a feature extraction and a rule decision. In the process of feature extraction, the time–frequency representation (TFR) has been designed for maximizing the separability between classes representing different faults. The diagnosis is realised in two levels; the first one allows the detection of different faults—bearing fault, stator fault and rotor fault. The second one refines this detection by the determination of severity degree of faults, which are already identified on the previous level. The diagnosis is independent of the level of load. This method is validated on a 5.5 kW induction motor test bench.