Incipient Bearing Fault Detection via Motor Stator Current Noise Cancellation Using Wiener Filter

Current-based monitoring can offer significant economic savings and implementation advantages over traditional vibration monitoring for bearing fault detection. The key issue in current-based bearing fault detection is to extract bearing fault signatures from the motor stator current. Since the bearing fault signature in the stator current is typically very subtle, particularly when the fault is at an incipient stage, it is difficult to detect the fault signature directly. Therefore, in this paper, the bearing fault signature is detected alternatively by estimating and removing nonbearing fault components via a noise cancellation method. In this method, all the components of the stator current that are not related to bearing faults are regarded as noise and are estimated by a Wiener filter. Then, all these noise components are cancelled out by their estimates in a real-time fashion, and a fault indicator is established based on the remaining components which are mainly caused by bearing faults. Machine parameters, bearing dimensions, nameplate values, and the stator current spectrum distribution are not required in the method. The results of online experiments with a 20-hp induction motor under multiple load levels have confirmed the effectiveness of this method.     

基于定子电流监测方法的电机故障诊断

电机是一种复杂的旋转机械,其故障种类多而且难以辨别。为了对小功率异步电机的常见故障进行正确快速地诊断,采用小波分析技术对几种常见故障所引起的定子电流变化进行了多分辨率分析。从能量分布的角度出发,快速且较为准确地诊断出电机故障。诊断效果表明,小波分析是对电机故障进行定子电流诊断的有效方法,同时为电机故障在线实时诊断提供了理论依据。     

基于定子电流监测方法的电机故障诊断

电机是一种复杂的旋转机械,其故障种类多而且难以辨别。为了对小功率异步电机的常见故障进行正确快速地诊断,采用小波分析技术对几种常见故障所引起的定子电流变化进行了多分辨率分析。从能量分布的角度出发,快速且较为准确地诊断出电机故障。诊断效果表明,小波分析是对电机故障进行定子电流诊断的有效方法,同时为电机故障在线实时诊断提供了理论依据。     

基于鲁棒最小二乘支持向量机的电机振动故障诊断

对时序数据建模与辨识技术进行了分析，提出了使用鲁棒最小二乘支持向量机(LS-SVM)算法建立自回归移动平均(ARMA)时序预测模型。该模型是在LS-SVM的约束条件中考虑了鲁棒特性和时序模型参数，使之在求解的过程中对孤立点与噪声不敏感，并且能准确地辨识时序模型参数。考虑到电机振动故障诊断的输入输出数据集间存在着复杂非线性时序上的关系，通过用基于鲁棒LS-SVM的ARMA模型预报电机的振动值，从而预测电机振动故障。实验表明该模型在对非线性时间序列预测精度和稳定性上具有明显的优越性，为确保电机正常运行创造了良好条件。     

基于感测线圈和KPCA的电机轴承故障检测

在电机定子中嵌入感测线圈并结合核主元分析(KPCA)进行电机轴承的故障检测。8只线圈分别嵌入电机定子的前端和后端,通过分时复用形成6组反应电机状态的差动信号。针对6组感测信号与电机状态间的非线性特征,采用KPCA进行多变量分析。着重介绍分析了KPCA故障检测的算法、监测指标和步骤。最后,用机车变压器油泵电机为对象开展了试验研究,验证了所提电机轴承故障监测方法的有效性和正确性。     

Identification of Intermittent Electrical and Mechanical Faults in Permanent-Magnet AC Drives Based on Time–Frequency Analysis

The detection of noncatastrophic faults in conjunction with other factors can be used to determine the remaining life of an electric drive. As the frequency and severity of these faults increase, the working life of the drive decreases, leading to eventual failure. In this paper, methods are presented to identify developing electrical and mechanical faults based on both the short-time Fourier transform and wavelet analysis of the field-oriented currents in permanent-magnet ac drives. The different fault types are classified by developing a linear discriminant classifier based on the transform coefficients.     

用频率偏移法检测直流系统故障

变电站直流系统接地故障的发生率一直排在电气故障率的首位,严重危害电力系统的安全运行。提出了一种新方法来检测直流系统故障——频率偏移法,阐述了其原理、工作方法、应用及优点。     

采用永磁频差电动机的自动平衡头

设计了使用永磁频差电动机的自动平衡头。以三相对称交流绕组作为内定子控制绕组,包括环形磁钢永磁转子的小车作为校正质量。计算了对应不同转子位置的空载气隙磁场分布,求出了静转矩特性和平衡头最高工作转速。     

经验模态分解法在电动机铁心故障检测中的应用

三相异步是电动机的定子铁心发生变形、松动或偏心,维护人员难以发现电动机这些异常情况的,但异步电动机的这些故障通常都将引起振动.因此可以通过检测电动机振动信号,用经验模态分解法和Hilbert变换的方法分析这一振动信号,以此形成衡量电动机故障程度的判据来判断电动机运行是否正常,尽早发现电动机运行故障.实验证明铁心发生故障...     

基于小波变换的异步电动机故障检测方法

建立了一般异步电动机动态数学模型。基于这个模型用Matlab中的Simulink技术模拟了电动机的两个常见故障：定子匝问短路和转子导条断裂。通过检测电动机定子电流,用coif4小波对电流信号进行分析,很容易判断出这两类故障。说明小波技术用于电机故障诊断是一个好的方法。     

Detection of Stator Winding Inter-Turn Short Circuit Fault in Induction Motor Using Vibration Signals by MEMS Accelerometer

This paper presents a novel method for detecting stator winding inter-turn short circuit fault in a three-phase induction motor by vibration signature analysis using non-electrical contact type microelectromechanical systems (MEMS) accelerometer. The present work detects inter-turn short circuit by vibration analysis in a three-phase induction motor running under different load conditions. Theoretical analysis of stator winding under inter-turn short circuit condition has been presented. Experimental investigations have been carried out to detect inter-turn short circuit fault by vibration signature analysis using a MEMS accelerometer with three-phase induction motor operating under different conditions. The spectral analysis of motor vibration has been carried out using radix-2 decimation in time fast Fourier transform algorithm. The appearance of particular characteristic frequency components in vibration spectrum indicates stator winding inter-turn short circuit faults. The experimental results are found to closely match with the theoretical values, indicating that MEMS accelerometers can be successfully employed for the detection of stator winding inter-turn short circuit fault in induction motors.     

Time‐varying linear systems and input–output stability

One useful strategy for establishing input–output stability is to seek conditions on the system map so that a certain lower‐bound condition is met. We show here that, for a large class of time‐varying linear systems, the lower‐bound condition for stability is in fact necessary. Copyright © 2000 John Wiley & Sons, Ltd.     

多尺度分析在感应电动机故障诊断中的应用

在对感应电机进行故障诊断的时候,扩展卡尔曼滤波器(EKF)只能在时域上估计电机转子的位置和速度.针对这一不足,提出了一种基于EKF和小波变换的多尺度诊断方法;通过测量电机的端电压和定子线圈电流在线估计电机转子的位置和速度.新算法结合了EKF和小波变换的优点,不仅能够分析出系统在频域上的特性,而且估计精度还要优于EKF.仿真结果说明了新算法的正确性和有效性.     

直流电动机的故障检测和诊断技术综述

本文对直流电动机主要故障表现特征进行总结,对相关的主要故障检测和诊断方法进行介绍和评述,提出直流电动机故障诊断的发展方向。     

Induction Motor Failure Detection Using Geometric Signal Separation

We present a new method for on-line induction motor failure detection using stator current monitoring. The concept of geometric signal separation in feature space is introduced. A set of observations from a single phase of the stator current is transformed into a feature vector. After establishing a local metric in the feature space, close neighbors of the present vector are searched for in a database representing allowed states of the motor, which were recorded during a training period. In their absence the present state is a novelty, which is considered as a failure if it persists for a certain time. This monitoring scheme successfully deals with varying load conditions of the motor, where also oscillating load torques can be tackled. Additionally, observations from a healthy motor, which differ considerably from any known allowed state in the database due to changes of environmental conditions can be distinguished from true failures at a significant level. We do not use expert rules or detailed knowledge about the monitored motor.     

基于改进的MCSA法的变频电机转子故障诊断

提出一种基于改进的定子电流信号分析(MCSA)和模糊推理的变频电机转子故障诊断方法.在传统MCSA方法的基础上提出的电机故障特征频率自选频算法,能够根据电机的实际运行状态更有效地提取故障特征量,为电机故障诊断提供依据.基于模糊集理论和模糊逻辑的模糊推理算法,则通过对获取的故障特征量进行模糊推理进而确定故障类型,对存在多种故障的电机尤为有效.试验结果表明,该方法能够提高变频电机转子故障诊断的准确性.     

基于HHT变换的电动机故障信号频率分析

提出将HHT变换(Hilbert-HuangTransform,HHT)中边际谱理论应用于电动机故障信号的HHT分解和频率跟踪研究的新方法,并通过实例验证Hilbert-Huang变换在电动机故障信号频率分析中的应用。     

基于HHT变换的电机故障信号频率分析

提出了将HHT变换(Hilbert-HuangTransform,简称HHT)中边际谱应用于电机故障信号频率跟踪研究的新方法。采用HHT将故障信号分解成若干个平稳的IMF(Intrinsic Mode Func-tion)分量,求出每1个IMF分量的瞬时幅值和瞬时频率。同时,指出了边际谱与Fourier频谱之间本质区别。利用边际谱的线性性质说明了边际谱的谱值表示信号中某一频率在各个时刻的幅值之和。最后通过实例验证了Hibert-Huang变换在电机故障信号中频率分析中的应用。     

Hilbert-Huang变换在电力故障暂态信号检测中的应用

提出利用Hilbert-Huang变换对电力系统故障信号检测的方法.通过对检测点获得的故障电流信号进行经验模式分解(EMD),得到一系列的本征模态分量(IMF),利用Hilbert变换得到Hilbert谱及边际谱,分析发现,通过瞬时频率突变能准确定位故障时刻,Hilbert谱的峰值变化也能反映故障时刻及故障特征信息;通过边际谱分析可以获得故障信号所含的真实频率.为进一步故障检测提供了依据.仿真试验表明Hilbert-Huang变换的方法能准确地检测故障时刻.     

感应电动机轴承故障检测的相关分析方法

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,提出了一种基于相关分析的感应电动机轴承故障检测方法.该方法先求三相电流的Park矢量模信号,然后将其与电机滚动轴承振动信号进行相关分析,从振动信号与电流信号的相关谱图中有效地提取轴承故障特征信息,并将其作为轴承故障识别的依据.实验结果表明,该检测方法具有较高的信噪比,可提高故障识别的精度,实际应用也是可行的.