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.     

基于MRAS的感应电动机无速度传感器控制方法

针对传统的感应电动机转速辨识算法只对电动机转速进行辨识,而没有考虑定子电阻的变化对电动机转速辨识所带来影响的问题,提出了一种改进的基于MARS的感应电动机无速度传感器控制方法。该方法采用电压模型的输出作为转子磁链、定子电阻的期望值,电流模型的输出作为转子磁链、定子电阻的推算值,根据MARS理论,以电压模型作为参考模型、电流模型作为自适应可调模型,进行电动机转速、定子电阻的辨识。仿真结果表明,该方法能够同时辨识电动机转速和定子电阻,有效消除了定子电阻发生变化对电动机转速辨识带来的影响,提高了感应电动机控制系统的低速辨识性能。     

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.     

定子电阻辨识用于异步电机温度监测的研究

定子绕组温度监测是电机安全运行的重要保障.利用定子电阻值与温度的密切关系,提出了一种定子电阻估计方法以反映绕组温度的变化.在电流定向的坐标系中,异步电机数学模型中与定子电阻相关的部分得到简化.基于异步电机稳态模型,得到了独立于定、转子电阻和转速的稳态磁链估计,然后利用稳态磁链建立了定子电阻在线整定律.该整定律只需量测定子电流电压,简单易行.所提出的定子温度监测方法可以作为电机过热保护的一种辅助措施.     

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.     

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.     

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.     

一种无速度传感器矢量控制系统的研究

本文基于同步轴系下的感应电动机电压磁链方程式,提出了一种感应电动机转子磁场定向的矢量控制方法,利用在同步轴系中q轴电流的误差信号实现对电机速度的估算。在该无传感器矢量控制系统中,由于采用了经典的PI调节器,使得控制系统更为简单。最后利用MATLAB建立的该无传感器矢量控制系统的仿真模型,通过仿真验证了本文所提出的无传感器矢量控制系统具有良好的动态和静态性能。     

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.     

Fault-tolerant cruise control of electric vehicles with induction motors

A fault-tolerant control scheme is proposed for the cruise control of electric vehicles (trains, cars) that make use of induction motors. It relies on a rotor speed reference generator and on a flux observer which is adaptive with respect to the uncertain rotor and stator resistances and to the load torque as well. The closed loop on-line identification of those three critical uncertain parameters allows for: (i) on-line estimating and imposing the motor flux modulus reference value which minimizes power losses at steady-state and improves power efficiency; (ii) the on-line detection of speed sensor faults as well as the fast switching on redundant motor speed sensors. CarSim simulations illustrate the effectiveness of the proposed approach.     

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。     

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.     

High gain observer based on-line rotor and stator resistances estimation for IMs

One of the main challenging issues in induction motor drives is the lack of knowledge about the actual values of some critical parameters, such as rotor and stator resistances which are subject to large variations during operation. Such problem is difficult to resolve due to the strong interconnection between states and parameters in the nonlinear motor model, besides the unavailability of both rotor flux and load torque. In the spirit to accurately follow the on-line machine variables, this paper focuses on the simultaneous estimation of internal states and time varying parameters. Especially, a new identification scheme for rotor resistance and/or stator resistance is introduced. In the aim of decoupling the unknown electrical parameters, we adopt a mild change of coordinates that allows to easily design a two-stage of high gain observer. The simplicity of presented procedures and the efficiency for real time computation constitute both main features of the proposed approach. Moreover, possible exploitation of our algorithm in the fault detection issue is discussed through a simulation of an abrupt rotor short-circuit.     

感应电机二阶滑模次优算法定子磁链观测器设计

提出了基于二阶滑模次优算法的感应电机定子磁链观测方法,设计了定子磁链观测器,并应用到感应电机直接转矩控制中.本文设计的磁链观测器,通过准确的跟踪电流及其变化率,从而实现对转子磁链的准确估算,然后利用转子磁链与定子磁链的关系,估算出定子磁链.由于本文设计的定子磁链观测器是一个多输入多输出(MIMO)系统,稳定性分析非常复杂,为此将磁链估算误差的微分看作扰动处理,从而将MIMO的观测器模型分解成两个独立的单输入单输出(SISO)系统,简化了稳定性分析.将该观测器用于感应电机直接转矩控制中,达到了很好的控制效果.仿真和实验验证了该方法的有效性.     

一种在线测算交流永磁同步电机定子电感参数的方法

本文介绍了一种采用准电流定向控制在线测算交流永磁同步电机定子电感参数的新方法,该方法利用通用的PWM调制的电压型逆变器在电机定子d轴上施加了一个正负交替的小电压,与d轴上获得的正负交替的小电流一起推算出定子电感参数。该法具有测试时间短和电机无需堵转的特点。它所涉及到的数学计算简单易于数字编程实现。MATLAB仿真结果证明了该方法的可行性和正确性。     

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.     

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。     

A new scheme for sensorless induction motor control drives operating in low speed region

A novel simple stator resistance estimation technique for high-performance induction motor drives is proposed. It makes use of a synchronously revolving reference frame aligned with the stator current vector, so that the resistance can be straightforwardly derived from the mathematical model of the induction motor. A sensorless direct field orientation scheme is employed to validate the proposed solution, with the drive operating in the critical area of low speeds. A combination of two observers is used: a Kalman filter observer to estimate the rotor flux, and a MRAS observer for speed estimation. The stator resistance estimator alleviates the usual performance degradation of MRAS-based drives at low speeds, caused by the thermal drift of stator resistance. Computer simulations, including realistic disturbances, show high effectiveness of the described approach.     

基于定转子电阻误差补偿的感应电动机自适应逆解耦控制研究

对于具有多变量、非线性、强耦合、慢时变等特征的异步电动机调速系统, 实现定子磁链与电磁转矩的高精度动态解耦是提高系统性能的关键. 首先通过非线性状态反馈建立感应电动机的积分逆模型, 并在此基础上提出了一个基于定、转子电阻误差补偿的感应电动机自适应逆解耦控制方法, 将补偿后的积分逆模型串联到对象的输入端建立广义被控对象. 复杂的感应电动机调速系统被解耦成电磁转矩与定子磁链的两个独立回路, 利用线性系统理论分别对独立回路进行综合设计, 实现定子磁链和电磁转矩对各自给定值的渐近跟踪. 利用Matlab进行了仿真实验, 实验结果验证了建议方案的有效性和可行性.     

Adaptive sensorless robust control of AC drives based on sliding mode control theory

This paper focuses in the design of a new adaptive sensorless robust control to improve the trajectory tracking performance of induction motors. The proposed design employs the so‐called vector (or field oriented) control theory for the induction motor drives, being the designed control law based on an integral sliding‐mode algorithm that overcomes the system uncertainties. This sliding‐mode control law incorporates an adaptive switching gain in order to avoid the need of calculating an upper limit for the system uncertainties. The proposed design also includes a new method in order to estimate the rotor speed. In this method, the rotor speed estimation error is presented as a first‐order simple function based on the difference between the real stator currents and the estimated stator currents. The stability analysis of the proposed controller under parameter uncertainties and load disturbances is provided using the Lyapunov stability theory. The simulated results show, on the one hand that the proposed controller with the proposed rotor speed estimator provides high‐performance dynamic characteristics, and on the other hand that this scheme is robust with respect to plant parameter variations and external load disturbances. Finally, experimental results show the performance of the proposed control scheme. Copyright © 2006 John Wiley & Sons, Ltd.