基于定子起动电流局域波分析的鼠笼异步电动机转子故障检测

论述了局域波分析方法的基本原理及特点,该方法源于瞬时频率的概念,它能把动态信号的局部特征准确地在时频域内予以描述;分析了异步电动机起动过程中转子故障特征量(1-2s)f1(f1为电网频率)分量的变化规律,提出了基于定子起动电流局域波分析的异步电动机转子故障特征提取新方法并应用到电机故障特征提取中。对实测数据进行处理的结果表明,该方法能够有效地检测出转子故障。     

变频供电异步电动机转子断条故障诊断

异步电机转子断条故障发生时,定子电流（变频器输出侧电流）中会出现对称频率（1±2s）f1（f1为定子电流频率）的故障特征附加电流信号。以此为依据,定子电流特征频谱分析（MCSA）发展为经典转子断条故障在线检测方法。在工程实际过程中,变频供电异步电动机容易采集到的信号是开关柜二次侧供电电流（变频器输入侧电流）．因此要实现变频异步电动机转子断条故障诊断,必须清楚供电电流中是否也含有断条故障特征信息。首次对变频异步电动机供电电流进行分析．得出供电电流中也包括转子断条故障特征信息的结论,以此为基础。利用连续细化傅立叶和自适应滤波相结合的方法,实现了变频异步电动机转子断条故障诊断。     

基于定子电流谐波的异步电动机转速测量研究

提出了一种测量封闭体内电动机转速的方法。通过测量异步电动机定子电流内的转子槽谐波频率分量和偏心频率分量,辨识出转子槽数,用转子槽谐波频率和转子槽数获得电动机的转速。     

基于DSP和LabVIEW的电动机转子断条故障诊断

为了实现对鼠笼式异步电动机转子断条故障的实时诊断,设计了一套以TMS320F2812和LabVIEW为核心的转子断条故障诊断装置。该装置以TMS320F2812为主控芯片,实现对异步电动机定子侧电流信号的采集,通过DSP的串口模块将采集到的信号传送到由LabVIEW构建的上位机信号处理平台。针对定子侧电流信号中工频分量对断条故障特征分量的干扰较大,严重地影响断条分量的识别,LabVIEW处理平台采用自适应陷波器算法对工频信号进行陷波处理,在FFT频谱上实现对断条故障特征分量的识别。实验证明,该装置能够实现对断条故障进行实时监测,并能够在FFT频谱上对断条故障特征分量进行识别。     

电流特征分析在异步电动机故障诊断中的应用

在线运行条件下,采集笼型异步电动机的定子电压和电流作为检测电动机故障的状态参数,并对电压和电流信号进行频谱分析,可以准确地检测出包括定子转子断条、绕组匝间短路、轴承磨损等各种常见的故障类型。     

采用交叉耦合补偿的异步电动机矢量控制系统研究

本文在异步电动机的矢量控制的基础上,依据转子磁场定向的基本原理,和基于同步轴系下的异步电动机电压磁链方程式,提出了一种三相异步电动机转子磁场定向的矢量控制方法,利用在同步轴系中T轴电流的误差信号实现对电动机速度的估算.在该无传感器矢量控制系统中,采用了经典的PI调节器,通过调节定子电流M轴分量来调节转子磁通,使得控制系统更为简单.     

变频器供电下异步电动机转子断条故障仿真研究

分析了变频器供电时所产生的谐波对电动机转子断条故障电流信号的影响,基于Matlab／Simulink建立了直接转矩控制变频器供电下异步电动机转子故障系统仿真模型,在变频器不同供电频率下针对电动机转子正常和故障情况进行仿真分析。理论上难了变频器谐波影响下电动机转子故障特征频率的正确性,利用连续细化傅立叶变换和自适应滤波方法实现了变频器不同频率供电下民步电动机转子故障的在线检测。     

转角不对中转子系统的电机电流特性

为了通过监测电机电流以实现转子系统故障的识别与诊断,建立偏角不对中转子系统动力学模型,推导出转子系统的转角不对中力矩,并以电机的电磁扭矩为纽带,在MATLAB/Simulink环境下建立三相异步电动机—转子系统机电耦合仿真模型,应用傅里叶变换对定子电流信号进行频谱分析,研究偏角不对中故障激励下电机电流信号的耦合特性。仿真结果表明:正常状态下,电流信号会产生工频分量和奇数次倍频的谐波分量。当转子系统存在转角不对中故障时,三次谐波分量会出现明显的增强,而且其峰值会随着转角不对中量的增加而变大。     

异步电动机故障诊断的小波变换等高图方法

用Morlet小波作为小波基，对异步电动机鼠笼转子故障时的定子电流信号进行多尺度分析，将获得的小波变换系数用等高图表示，从中能清楚地识别出异步电机鼠笼转子不同断条的故障。较基于傅立叶变换的故障诊断，该方法对异步电动机故障的辩识能力有显著提高。     

基于精确信息重构的故障转子系统振动加速度信号积分方法

在分析总结加速度信号时域积分法与频域积分法优缺点基础上,提出一种基于精确信息重构的故障转子系统振动加速度信号积分方法。该方法利用故障转子系统振动信号由转频、倍频及分倍频分量构成为主的特点对加速度信号进行快速傅里叶变换(Fast Fourier transform,FFT),通过特征频率分量提取并将所提取的分量的幅值与预设的阈值进行比较;幅值低于阈值的分量认为是噪声分量,予以剔除,高于阈值的分量保留并进行精确频谱校正;根据校正后各个频率分量的频率、幅值和相位积分重构出相应的速度信号和位移信号。精确信息重构方法在去除宽频噪声和保留有用特征信息方面有明显的优势。最后通过仿真分析和试验验证,结果表明该方法相对于传统的时域和频域积分具有更高的精度和优越性。     

Online Detection of Broken Rotor Bar Fault in Induction Motors by Combining Estimation of Signal Parameters via Min-norm Algorithm and Least Square Method

Current research in broken rotor bar (BRB) fault detection in induction motors is primarily focused on a high-frequency resolution analysis of the stator current. Compared with a discrete Fourier transformation, the parametric spectrum estimation technique has a higher frequency accuracy and resolution. However, the existing detection methods based on parametric spectrum estimation cannot realize online detection, owing to the large computational cost. To improve the efficiency of BRB fault detection, a new detection method based on the min-norm algorithm and least square estimation is proposed in this paper. First, the stator current is filtered using a band-pass filter and divided into short overlapped data windows. The min-norm algorithm is then applied to determine the frequencies of the fundamental and fault characteristic components with each overlapped data window. Next, based on the frequency values obtained, a model of the fault current signal is constructed. Subsequently, a linear least squares problem solved through singular value decomposition is designed to estimate the amplitudes and phases of the related components. Finally, the proposed method is applied to a simulated current and an actual motor, the results of which indicate that, not only parametric spectrum estimation technique.     

IBPSO-Based MUSIC Algorithm for Broken Rotor Bars Fault Detection of Induction Motors

In spectrum analysis of induction motor current, the characteristic components of broken rotor bars (BRB) fault are often submerged by the fundamental component. Although many detection methods have been proposed for this problem, the frequency resolution and accuracy are not high enough so that the reliability of BRB fault detection is affected. Thus, a new multiple signal classification (MUSIC) algorithm based on particle swarm intelligence search is developed. Since spectrum peak search in MUSIC is a multimodal optimization problem, an improved bare-bones particle swarm optimization algorithm (IBPSO) is proposed first. In the IBPSO, a modified strategy of subpopulation determination is introduced into BPSO for realizing multimodal search. And then, the new MUSIC algorithm, called IBPSO-based MUSIC, is proposed by replacing the fixed-step traversal search with IBPSO. Meanwhile, a simulation signal is used to test the effectiveness of the proposed algorithm. The simulation results show that its frequency precision reaches 10^?5, and the computational cost is only comparable to that of traditional MUSIC with 0.1 search step. Finally, the IBPSO-based MUSIC is applied in BRB fault detection of an induction motor, and the effectiveness and superiority are proved again. The proposed research provides a modified MUSIC algorithm which has sufficient frequency precision to detect BRB fault in induction motors.     

参数不确定条件下基于 I&I 的异步电机鲁棒控制新方法

针对不确定参数存在条件下异步电机( IM )难以精确进行动态控制的问题,提出了一种基于浸入与不变性( I&I )理论的鲁棒控制方法。在故障工况(转子绕组渐增及负载突变)导致异步电机电气参数存在不确定性的情况下,基于 IM 系统动力学和 I&I 基本原理,通过设计补偿器实现了电机的鲁棒控制,并基于 Lyapunov 直接稳定性方法证明了所设计控制器的渐近稳定性。最后,通过仿真和实验对所提控制方法的有效性和适用性进行了验证。结果表明,所提出的控制方法在参数不确定条件下鲁棒性良好,且在参考信号类型改变时, IM 输出信号仍能够准确、快速地跟踪参考信号。     

Detection of broken rotor bars in induction motors using nonlinear Kalman filters

This paper presents a model-based fault detection approach for induction motors. A new filtering technique using Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF) is utilized as a state estimation tool for on-line detection of broken bars in induction motors based on rotor parameter value estimation from stator current and voltage processing. The hypothesis on which the detection is based is that the failure events are detected by jumps in the estimated parameter values of the model. Both UKF and EKF are used to estimate the value of rotor resistance. Upon breaking a bar the estimated rotor resistance is increased instantly, thus providing two values of resistance after and before bar breakage. In order to compare the estimation performance of the EKF and UKF, both observers are designed for the same motor model and run with the same covariance matrices under the same conditions. Computer simulations are carried out for a squirrel cage induction motor. The results show the superiority of UKF over EKF in nonlinear system (such as induction motors) as it provides better estimates for rotor fault detection.     

IBPSO-Based MUSIC Algorithm for Broken Rotor Bars Fault Detection of Induction Motors

In spectrum analysis of induction motor current, the characteristic components of broken rotor bars(BRB) fault are often submerged by the fundamental component. Although many detection methods have been proposed for this problem, the frequency resolution and accuracy are not high enough so that the reliability of BRB fault detection is a ected. Thus, a new multiple signal classification(MUSIC) algorithm based on particle swarm intelligence search is developed. Since spectrum peak search in MUSIC is a multimodal optimization problem, an improved bare?bones particle swarm optimization algorithm(IBPSO) is proposed first. In the IBPSO, a modified strategy of subpopulation determination is introduced into BPSO for realizing multimodal search. And then, the new MUSIC algorithm, called IBPSO?based MUSIC, is proposed by replacing the fixed?step traversal search with IBPSO. Meanwhile, a simulation signal is used to test the e ectiveness of the proposed algorithm. The simulation results show that its frequency precision reaches 10~(-5), and the computational cost is only comparable to that of traditional MUSIC with 0.1 search step. Finally, the IBPSO?based MUSIC is applied in BRB fault detection of an induction motor, and the e ectiveness and superiority are proved again. The proposed research provides a modified MUSIC algorithm which has su cient frequency precision to detect BRB fault in induction motors.     

感应电机轴承故障检测方法研究

分析了感应电机轴承发生故障时的振动信号的特性,利用带通滤波器和希尔伯特变换,对感应电机轴承振动信号进行处理,然后采用高分辨率谱估计算法--MUSIC（Multiple Signal Classification）算法对包络信号作谱分析,再从包络信号的MUSIC谱中提取故障特征频率分量.研究结果表明,该方法频率分辨率更高,故障检测更为准确.将该方法应用于电机轴承故障诊断,可准确提取轴承故障特征分量.     

鼠笼电机转子断条故障的定子电流信号平方解调分析诊断方法

受基频频谱泄露影响,经典MCSA方法诊断鼠笼电机转子断条故障时的诊断能力严重依赖于电机负载大小。针对这一问题,提出了基于定子电流信号平方解调制分析诊断方法。首先采用硬件方式对定子电流信号作基于平方解调制的信号预处理,以此消除制约诊断能力的基频频谱泄露,继而对解调后的信号作快速傅里叶变换,然后根据频谱中是否存在特征频率成分判断转子断条故障发生与否。在3 k W电机实验平台上对所提出的方法进行实验验证。实验结果表明,即使鼠笼电机在轻载或空载条件下运行时所提出的方法仍然能够诊断出转子断条故障,从而有效提高了诊断能力。     

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.     

Detection of a static eccentricity fault in a closed loop driven induction motor by using the angular domain order tracking analysis method

In this study, a new method was presented for the detection of a static eccentricity fault in a closed loop operating induction motor driven by inverter. Contrary to the motors supplied by the line, if the speed and load, and therefore the amplitude and frequency, of the current constantly change then this also causes a continuous change in the location of fault harmonics in the frequency spectrum. Angular Domain Order Tracking analysis (AD-OT) is one of the most frequently used fault diagnosis methods in the monitoring of rotating machines and the analysis of dynamic vibration signals. In the presented experimental study, motor phase current and rotor speed were monitored at various speeds and load levels with a healthy and static eccentricity fault in the closed loop driven induction motor with vector control. The AD-OT method was applied to the motor current and the results were compared with the traditional FFT and Fourier Transform based Order Tracking (FT-OT) methods. The experimental results demonstrate that AD-OT method is more efficient than the FFT and FT-OT methods for fault diagnosis, especially while the motor is operating run-up and run-down. Also the AD-OT does not incur any additional cost for the user because in inverter driven systems, current and speed sensor coexist in the system. The main innovative parts of this study are that AD-OT method was implemented on the motor current signal for the first time.