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...     

Stator winding fault prediction of induction motors using multiscale entropy and grey fuzzy optimization methods

In the present work, stator winding fault prediction is studied using a multiscale entropy (MSE) algorithm combined with a grey-based fuzzy algorithm. Experiments were performed with a normal motor and a motor with faulty stator winding. Real time, motor current and vibration signals were acquired at different operating speeds and were used for the diagnosis of faults. The obtained signals were denoised by wavelet transform. Grey relational analysis (GRA) coupled with fuzzy logic was used to model the stator winding fault and to predict the optimal setting for running the induction motor within its parameters range. Analysis of variance (ANOVA) was performed to determine the effect of each individual parameter on the response. The results indicate that the proposed novel approach is very effective in predicting the stator winding fault. Furthermore, the best running parameters for the induction motor are also reported.     

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.     

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。     

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.     

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.     

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.     

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.     

A novel multi-agent approach to identify faults in line connected three-phase induction motors

Three-phase induction motors (TIMs) are the key elements of electromechanical energy conversion in a variety of productive sectors. Identifying a defect in a running motor, before a failure occurs, can provide greater security in the decision-making processes for machine maintenance, reduced costs and increased machine operation availability. This paper proposes a new approach for identifying faults and improving performance in three-phase induction motors by means of a multi-agent system (MAS) with distinct behavior classifiers. The faults observed are related to faulty bearings, breakages in squirrel-cage rotor bars, and short-circuits between the coils of the stator winding. By analyzing the amplitudes of the current signals in the time domain, experimental results are obtained through the different methods of pattern classification under various sinusoidal power and mechanical load conditions for TIMs. The use of an MAS to classify induction motor faults allows the agents to work in conjunction in order to perform a specific set of tasks and achieve the goals. This technique proved its effectiveness in the evaluated situations with 1 and 2 hp motors, providing an alternative tool to traditional methods to identify bearing faults, broken rotor bars and stator short-circuit faults in TIMs.     

基于融合分析的感应电机轴承故障检测方法

根据感应电机轴承发生故障时的振动信号特性以及定子电流特性，求出三相电流的Park矢量模信号，并将其与电机滚动轴承振动信号经解调处理后的包络信号进行融合分析。可以从振动信号与电流信号的融合谱图中有效地提取轴承故障特征信息，并将其作为故障识别的依据。实验结果表明，本文检测方法具有较高的信噪比，提高了诊断的可靠性。     

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。     

基于RBF神经网络的电机故障诊断的研究

在对国内外感应电动机故障诊断技术发展与研究的基础上,提出了从定子电流人手,利用径向基（RBF）神经网络算法来监测感应电动机工作状态,从而实现对电动机较为常见的电气故障和机械故障的综合检测。Matlab仿真结果表明RBF算法有效地实现了对电机故障诊断的研究。     

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。     

DCT-based differential relay for improved protection of generators

The paper reports the development of a three-phase differential relay for protection of stator windings of large generators around an MC-68000 microprocessor. The relay compares the average values of the fundamental frequency components of the differential and sum currents, which are obtained from the stator winding currents using a discrete cosine-transform-based filter. A variable-bias operating characteristic has been selected for the relay to achieve stability on heaviest external faults and ensure high sensitivity on light internal faults. The relay operating time varies from 1·25 to 30 ms depending on the fault severity.     

异步电动机故障诊断方法研究

介绍了基于Park矢量变换的故障诊断方法、基于傅里叶变换的定子电流频谱分析、基于小波分析的故障诊断方法的原理,并分析探讨了该3种故障诊断方法在三相笼型异步电动机故障诊断中的应用效果。     

FDI based on an adaptive observer for current and speed sensors of PMSM drives

This paper deals with a new method of current and speed sensors faults detection isolation (FDI) and identification for a permanent magnet synchronous motor (PMSM) drives. A new state variable is introduced so that an augmented system can be constructed to treat PMSM sensor faults as actuator faults. This method uses the PMSM model and a bank of adaptive observers to generate residuals. The residuals results are used for sensor fault detection. A logic algorithm is built in such a way to isolate and identify the faulty sensor for a stator phase current fault after detecting the fault occurrence. Simulation results are presented to illustrate the functionality of theoretical developments. Experimental results with 1.1-kW PMSM have validated the effectiveness of the proposed FDI method. The experimental implementation is carried out on powerful dSpace DS1103 controller board based on the DSP TMS320F240.     

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.     

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.     

Decision-level fusion based on wavelet decomposition for induction motor fault diagnosis using transient current signal

In this paper, we propose and implement a decision-level fusion model by combining the information of multi-level wavelet decomposition for fault diagnosis of induction motor using transient stator current signal. Firstly, the start-up transient current signals are collected from different faulty motors. Then signal preprocessing is conducted containing smoothing and subtracting to reduce the influence of line frequency in transient current signals. Next, we employ discrete wavelet transform technique to decompose the preprocessed signals into different frequency ranges of products, and then features are extracted from decomposed detail components. Finally, two decision-level fusion strategies, Bayesian belief fusion and multi-agent fusion, are employed. That is, fault features are classified using several classifiers and generated decisions are fused using a specific fusion algorithm. The proposed approach is evaluated by an experiment of fault diagnosis for induction motors. Experiment results show that excellent diagnosis performance can be obtained.     

基于幅值恢复算法的谐波分析实验

本文的研究目的就是针对三相异步电机的运行状态的监测以期对电机早期故障能够及时发现,减小故障严重后造成的巨大损失。本文采用分析定子电流的方式对运行中的电机进行现场监测,为解决故障频率与电网频率接近,且电机轻微故障时,定子电流中故障特征分量幅值过小的问题,使用一种新的谐波分析方法——幅值恢复算法,将该算法结合Fourier频谱分析,可对电机轻微故障和微弱的谐波成分做出有效的分析。