改进双谱和经验模态分解在牵引电机轴承故障诊断中的应用

铁路机车传动系统的故障诊断,对保障列车安全可靠运行、防范事故发生起重要的作用。为了有效诊断牵引电机轴承的早期故障,提出基于经验模态分解和改进双谱的故障特征提取方法。经验模态分解是一种数据驱动的信号处理算法,相当于一个自适应滤波器组,其可将信号分解成占据不同频带的固有模态函数,实现信号消噪。滚动轴承承载运转时,局部损伤点以故障特征频率反复撞击与之接触的其它元件表面,会引发机械系统共振;基于此,采用改进双谱分析轴承振动信号各分量间的相互作用,检测轴承故障特征频率。机车实际运行试验表明,所提方法能准确诊断牵引电机轴承的早期故障。     

Motor bearing damage detection using stator current monitoring

This paper addresses the application of motor current spectral analysis for the detection of rolling-element bearing damage in induction machines. Vibration monitoring of mechanical bearing frequencies is currently used to detect the presence of a fault condition. Since these mechanical vibrations are associated with variations in the physical air gap of the machine, the air gap flux density is modulated and stator currents are generated at predictable frequencies related to the electrical supply and vibrational frequencies. This paper takes the initial step of investigating the efficacy of current monitoring for bearing fault detection by correlating the relationship between vibration and current frequencies caused by incipient bearing failures. The bearing failure modes are reviewed and the characteristic bearing frequencies associated with the physical construction of the bearings are defined. The effects on the stator current spectrum are described and the related frequencies determined. This is an important result in the formulation of a fault detection scheme that monitors the stator currents. Experimental results which show the vibration and current spectra of an induction machine with different bearing faults are used to verify the relationship between the vibrational and current frequencies. The test results clearly illustrate that the stator current signature can be used to identify the presence of a bearing fault     

基于ITD和LS - SVM的风力发电机组轴承故障诊断

为了更好地识别出复杂条件下风力风电机组主轴承的运行状态,提出了基于固有时间尺度分解(ITD)和最小二乘支持向量机(LS-SVM)的风电机组轴承故障诊断方法。该方法首先将调心滚子轴承振动信号分解成若干个固有旋转分量和一个趋势分量之和。然后,对前几个固有旋转分量的瞬时幅值进行频谱分析,找出频谱中外圈、内圈、滚动体故障特征频率处以及转动频率处的幅值,将其作为故障特征向量。最后,将故障特征向量输入LS-SVM来识别机组轴承的运行状态。实验结果表明,该方法可以快速、较准确地诊断出风力发电机组轴承故障。     

小波变换及经验模式分解方法在电机轴承早期故障诊断中的应用

电机轴承早期故障的有效诊断是实现安全生产、避免大事故的技术前提。文中用高精度加速度传感器采集电机轴承振动信号，采用小波变换实现信噪分离，提取淹没在噪声背景中的早期故障特征信息，然后对提纯的信号进行经验模式分解（EMD）而得到若干个基本模态分量（IMF），再计算各基本模态分量的频谱。理论及试验研究结果表明：按此方法得到的各基本模态分量的频谱突显了轴承的故障特征信息，能有效诊断出轴承的早期故障。     

Diagnosis tool for motor condition monitoring

In the modern industrial environment there is increasing demand for automatic condition monitoring. With reliable condition monitoring, faults such as mechanical motor failures could be identified in their early stages and further damage to the system could be prevented. Successful monitoring is a complex and application-specific problem, but a generic tool would be useful in preliminary analysis of new signals and in verification of known theories. A generic condition diagnosis tool is introduced in this paper. The tool is based on discriminative energy functions which reveal discriminative frequency-domain regions where failures can be identified. The tool was applied to induction motor bearing fault detection and succeeded in finding characteristic frequencies which allow accurate detection of bearing faults.     

Bearing fault detection via autoregressive stator current modeling

This paper proposes a method for detecting developing bearing faults via stator current. Current-based condition monitoring offers significant economic savings and implementation advantages over vibration-based techniques. This method begins by filtering the stator current to remove most of the significant frequency content unrelated to bearing faults. Afterwards, the filtered stator current is used to train an autoregressive signal model. This model is first trained while the bearings are healthy, and a baseline spectrum is computed. As bearing health degrades, the modeled spectrum deviates from its baseline value; the mean spectral deviation is then used as the fault index. This fault index is able to track changes in machine vibration due to developing bearing faults. Due to the initial filtering process, this method is robust to many influences including variations in supply voltage, cyclical load torque variations, and other (nonbearing) fault sources. Experimental results from ten different bearings are used to verify the proficiency of this method.     

Basic vibration signal processing for bearing fault detection

Faculty in the College of Engineering at the University of Alabama developed a multidisciplinary course in applied spectral analysis that was first offered in 1996. The course is aimed at juniors majoring in electrical, mechanical, industrial, or aerospace engineering. No background in signal processing or Fourier analysis is assumed; the requisite fundamentals are covered early in the course and followed by a series of laboratories in which the fundamental concepts are applied. In this paper, a laboratory module on fault detection in rolling element bearings is presented. This module is one of two laboratory modules focusing on machine condition monitoring applications that were developed for this course. Background on the basic operational characteristics of rolling element bearings is presented, and formulas given for the calculation of the characteristic fault frequencies. The shortcomings of conventional vibration spectral analysis for the detection of bearing faults is examined in the context of a synthetic vibration signal that students generate in MATLAB. This signal shares several key features of vibration signatures measured on bearing housings. Envelope analysis and the connection between bearing fault signatures and amplitude modulation/demodulation is explained. Finally, a graphically driven software utility (a set of MATLAB m-files) is introduced. This software allows students to explore envelope analysis using measured data or the synthetic signal that they generated. The software utility and the material presented in this paper constitute an instructional module on bearing fault detection that can be used as a stand-alone tutorial or incorporated into a course.     

Fault-signature modeling and detection of inner-race bearing faults

This paper develops a fault-signature model and a fault-detection scheme for using machine vibration to detect inner-race defects. To motivate this research, it is explained and illustrated with experimental results why fault signatures from nonouter-race defects (e.g., inner-race defects) can be less salient than those from outer-race defects. Then, a signal model is presented for the production and propagation of an inner-race fault signature; this model is then used to design an inner-race fault-detection scheme. This scheme examines machine-vibration spectra for peaks with phase-coupled sidebands occurring at a spacing predicted by the model. The proficiency of this fault-detection scheme at detecting inner-race bearing faults is then experimentally verified with results from 12 bearings representing varying degrees of fault severity.     

模拟电路故障检测与定位新方法

提出了一种基于统计理论与智能信息处理技术的容差模拟电路故障检测与定位新方法。在充分考虑容差效应的基础上,构建了故障阈值函数与故障判据,从而可通过监测可测点工作电压实现电路的故障在线检测。再通过离线测量电路在不同测试频率下输出对输入的增益,将可测点工作电压与电路增益两类测试信息经特征层融合,运用所提出的遗传神经网络方法对电路进行故障定位。仿真结果表明:所提方法对模拟电路的硬故障与元件参数偏移较小的软故障均适用,故障检测与定位准确率高。     

基于谱相干滤波冲击增强的轴承故障诊断

针对优化特征改进包络谱(IESFO)存在早期故障弱特征提取能力较弱和对频率分辨率要求较高的不足,提出了基于谱相干滤波冲击增强的轴承故障特征提取方法。首先使用IESFO算法选取优化解调频带并进行带通滤波;然后对滤波后信号使用多点优化最小熵反褶积(MOMEDA)算法增强信号中轴承故障产生的冲击;最后进行包络分析。基于实测信号的研究结果表明,和现有方法相比,本方法在轴承性能退化过程中可以提前提取到轴承早期故障信息,且可用于提取滚动轴承复合故障信息。     

双馈风力发电机故障诊断方法研究*

为了提高对双馈风机定、转子绕组故障和轴承故障监测的准确性,搭建了双馈风机的Simulink故障模型,得到不同故障的特征频率,通过对故障特征频率的理论分析验证了故障模型的准确性。采用Zoom FFT和多种加窗傅里叶变换来提取故障特征量,进行频谱分析并对比。实验结果表明,加rife-vincent窗FFT在处理双馈风机故障时具有较高的准确性、抗干扰性、可分辨性和可靠性;风速的变化影响故障特征频率,应根据实际风速的变化来调整故障参考值,以达到更好的双馈电机状态监测效果。     

基于决策融合的直驱风力发电机组轴承故障诊断

基于振动信号时域、频域和包络谱等多源特征,采用决策融合方法构建了直驱风力发电机组轴承故障诊断模型。对直驱风力发电机组主轴轴承经常发生的外圈故障、内圈故障、滚动体故障以及正常运行4种状态进行了实验研究。选取具有较高故障区分度,适合风电机组轴承故障诊断的特征参数。以风电机组振动信号的时域特征、频域特征和包络谱频域特征为诊断样本,使用灰色关联分析方法对机组轴承故障进行初步诊断,然后用证据融合理论对不同证据进行决策信息融合,从而获得最终诊断结果。实验结果表明,该方法能较好地识别风力发电机组轴承故障。     

电力变压器故障类型与关键状态量关联规则分析

传统电力变压器设备运维大多采用状态检修技术,但积累的状态监测和检测数据没有得到充分挖掘利用,造成信息资源的浪费。以故障特征量为前项,以故障类型为后项,设置最小支持度和最小置信度,运用Apriori数据挖掘经典算法挖掘出变压器故障和关键状态量之间的关联规则。基于关联规则挖掘原理,利用SPSS Modeler软件平台建立电力变压器故障关联规则挖掘模型进行分析,得出了故障诊断的具体流程,旨在采取关联规则挖掘的方法发现状态特征量和故障类别之间的内在联系,对故障进行判定。     

基于IMHSA-MSCNN-BiLSTM的风机轴承故障诊断

由于风力发电机组的非平稳运行条件和周围恶劣的工作环境,风机轴承故障振动脉冲特征易被随机噪声干扰所淹没,这给准确检测滚动轴承故障造成了挑战。为了降低随机干扰对后续特征提取的影响和算法复杂度,提出了一种改进多头自注意力机制（IMHSA）-多尺度卷积网络（MSCNN）-双向长短期记忆网络（BiLSTM）的风机轴承故障诊断方法。首先,由周期空洞自注意力和局部自注意力组成的IMHSA对特征进行增强,以减少随机干扰影响及特征增强过程中的时间消耗;然后,利用MSCNN-BiLSTM网络提取故障信号中的空间特征与长期依赖特征;最后,经全连接层和Softmax层输出风机轴承故障诊断结果,并采用实验台滚动轴承实际运行数据进行算例分析,通过与领域内其他同类方法的对比,验证了所提方法的有效性和优越性。     

Experimentally generating faults in rolling element bearings via shaft current

The purpose of this research is to develop a method for experimentally generating in situ bearing faults. To motivate this topic, experimental results are provided that illustrate how the act of removing and replacing test bearings drastically alters the machine vibration and stator current spectral characteristics. Based on this observation, a method is developed that employs an externally applied shaft current to initiate and progress a bearing fault in an accelerated timeframe. This experimental method begins with a new, undamaged bearing and progresses it throughout its entire lifecycle in situ. The test machine is a standard induction motor that can be interfaced with any load and operate at any arbitrary speed or load level throughout the bearing failure process. Data generated by this experimental method can then be used to evaluate the performance of various bearing condition monitoring schemes.     

考虑不平衡磁拉力的双馈异步风力发电机轴承外圈故障动力学建模

轴承故障会引起双馈异步风力发电机转子气隙变化,产生不平衡磁拉力(UMP)。为准确揭示双馈异步风力发电机轴承故障振动特性,开展了考虑UMP的双馈异步风力发电机轴承外圈故障动力学建模研究。首先,基于赫兹接触理论构建了轴承外圈故障模型;然后,推导了正常和轴承故障下发电机转子的气隙磁密,得到了发电机转子受到的UMP解析式;最后,采用Runge-Kutta法对模型进行求解,得到了轴承故障振动响应。试验分析表明：所提动力学模型能够有效揭示双馈异步风力发电机轴承故障振动信号的双冲击现象,UMP激励会影响风力发电机轴承外圈故障振动信号的调制特性。为风力发电机轴承故障诊断提供了新的理论参考。     

Stator current fault diagnosis of induction motor bearings based on the fast Fourier transform

Monitoring of the technical state of induction motor bearings is one of the important indicators of reliable and efficient operation of industrial electrical drives. This article considers the problems of fault diagnosis of induction motor bearings with a squirrel-cage rotor by stator consumption current. Test results on an experimental test bench with an electromagnetic powder brake are presented and analyzed. Experiments were carried out during tests of the induction motor at the rated mode with an operable bearing and alternately with two faulty bearings with defects at the inner and outer parts of cages. The characteristic harmonic components of stator currents appearing during operation of the induction motor with faulty bearings are presented. It is revealed that an anticipatory diagnosis of induction motors by stator current makes it possible to reveal the air-gap eccentricity due to the operation of an electric motor with a damaged bearing. Control of the technical state of bearings is important both during at the stage of manufacturing machines, in particularly for powerful and crucial mechanisms, and during their use. The basic features of monitoring the bearings are revealed, and recommendations for diagnosis by stator currents are presented.     

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.     

信息融合技术在电力系统故障检测中的应用探讨

电力系统故障产生各种故障信息，对故障信息全面分析、综合处理，能提高故障检测的精度和鲁棒性。为实现对各种传感器检测到的多源故障信息进行有机综合处理，需研究信息综合处理技术。信息融合技术是研究多源信息综合处理的新兴边缘学科，已在军事、信息处理等领域中有着成熟的应用。该文把信息融合技术应用于电力系统故障检测，介绍信息融合故障检测的模型与方法；分析信息融合技术在状态监测、继电保护中的应用技术；并以小电流接地系统故障选线为例，提出研究了模糊信息融合故障选线方法技术，提高了故障选线的灵敏度和可靠性。     

基于多信息融合的自适应单相接地故障在线定位研究与应用

为解决各种配电终端、故障指示器的故障信息、故障录波文件接入配电主站,实现配电主站配电网单相接地故障在线定位,根据配电网单相接地故障特征,提出应用相关系数、广义S变换提取零序电流、三相电流暂态录波的故障特征量以形成多源冗余信息,并采用模糊C均值聚类分析法融合多种故障特征量的在线定位的策略。仿真结果表明,该方案适用于各种接线方式、各种运行工况,定位准确率和可靠性高。