基于时延降噪循环双谱的电机轴承故障检测方法研究

当电机轴承出现故障时,定子电流信号中的微弱故障特征淹没在基波及谐波的强大噪声背景中。由于信噪比过低,基于电流的轴承故障检测一直是一个挑战。为此,提出了一种基于时延降噪的循环双谱检测方法。首先根据循环双谱函数构造定子电流解析表达式,提取电流解析式的特征解。通过理论分析确定循环双谱函数中的时延滞后量,减少电流信号中的无关噪声成分。最后利用实验平台采集定子电流,在电流解析式取得特征解的位置做切片频谱分析。实验结果表明,所提方法能有效提升信噪比,并提取出电流中非平稳的微弱故障特征,实现电机轴承故障的检测。     

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     

Detection of stator short circuit faults in three-phase induction motors using motor current zero crossing instants

Stator faults typically have a significant share amongst the common type of faults in industrial three-phase induction (asynchronous) motors. This paper presents a motor current signature analysis (MCSA)-based diagnostics of the stator winding short circuit fault. This type of fault happens due to the destruction of the turn insulation, and can be very detrimental causing motor shutdown. Instead of traditional MCSA using the motor stator current, in this paper, analysis using the zero crossing time (ZCT) signal of the stator current is presented. The theoretical aspects of the stator short circuit detection are presented. Following that, a diagnostic algorithm utilizing the ZCT signals is proposed. Experiments are performed with real motors, healthy and with shorted stator windings. Frequency analysis of the ZCT signals from the experimental data substantiates the theoretical arguments with significant accuracy.     

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.     

定子电流二次方法的异步电机转子复合故障诊断

为了突出故障早期的故障特征信息,避免复杂算法,实现故障早期状态在线监测,提出定子电流二次方法(流方)的笼型异步电机转子复合故障检测。定子电流经过二次方后转移和放大了定子电流中的故障信号,由于转子断条和气隙偏心的故障特征频率在频谱分布上的差距,转子复合故障得到分离和辨识。同时比较了单相流方和三相总流方对复合故障的诊断效果。复合故障时,流方的故障频率不是单一故障频率的直接累加,还包括两故障频率所引起的交叉干扰项。实验表明,随着断条数的增加,复合故障中表征断条的故障特征分量的幅值增加,有利于故障程度的量化和诊断系统的建立。     

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.     

Mechanical Load Fault Detection in Induction Motors by Stator Current Time-Frequency Analysis

This paper examines the detection of mechanical faults in induction motors by an original use of stator current time-frequency analysis. Mechanical faults lead generally to periodic load torque oscillations. The influence of the torque oscillations on the induction motor stator current is studied using an analytical approach. The mechanical fault results in a sinusoidal phase modulation of the stator current, which is equivalent to a time-varying frequency. Based on these assumptions, several signal processing methods suitable for stator current signature analysis are discussed: classical spectral analysis, instantaneous frequency estimation, and the Wigner distribution. Experimental and simulation results validate the theoretical approach in steady-state operating conditions     

Online computational tools dedicated to the detection of induction machine faults

This paper presents online components calculation techniques for stator and rotor of the induction machine. Four techniques have been developed for online components calculation; the first one starts the calculation of the negative component of stator current space vector using the Discrete Fourier Transform (DFT) in order to detect the stator fault. The second technique is dealing with the detection of rotor fault by the Recursive Fourier Transform (RFT), This technique improves the signal acquisition and enhanced detection of components near the fundamental. The third technique allows improving of the rotor fault detection by the spectrum of analytical signal. The fourth and the last technique is the frequency analysis of the instantaneous power, which allows obtaining a singular signature of faults. These techniques have shown better detection, where each fault is characterized by a singular signature and therefore they improve the detection and diagnosis of faults. Experimental results applied on an asynchronous machine 5.5 kW, approve and validate these calculation techniques.     

基于信息融合分析的感应电机故障检测方法

为了提高感应电机故障检测的准确性,在分析了感应电机定子线圈短路故障时的振动特征信息及定子电流的谱信息的基础上,指出了单一的振动分析方法或定子电流频谱分析（MCSA）诊断定子线圈短路故障,不能得到准确可靠诊断结果的原因,提出了一种基于信息融合分析的感应电机定子故障检测方法,能有效提取电机定子故障时的特征信息,提高了故障识别的准确性。实验结果证实,基于融合分析得到故障特征可以作为感应电机定子线圈短路故障诊断的依据。     

Detection of Winding Faults in Wound Rotor Induction Motor Using Loci of Direct and Quadrature Axes of Rotor Currents

In this paper, loci of direct and quadrature axis of rotor currents are used for online detection of winding fault on stator side as well as rotor side in three-phase wound rotor induction motor. The proposed technique is used for detecting load on induction motor and open circuit fault condition on stator side as well as rotor side. Detection of winding fault is affected by unbalanced supply voltage. In order to overcome this problem, FFT of modulus of direct and quadrature axis is used. The experimental results of rotor current analysis are validated with stator current analysis. Research works reported in the literature are based on stator currents analysis. The proposed technique of detecting winding faults and distinguishing between stator winding faults and unbalanced supply voltage using rotor current is not reported in the literature.     

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.     

异步电动机断条与静偏心复合故障的电流特征

为了研究笼型异步电动机断条和静态偏心复合故障特征,采用多回路数学模型和改进的绕组函数法对复合故障下的定子电流特征进行了机理分析和仿真研究。首先建立笼型异步电动机的多回路数学模型,通过改进的绕组函数法计算了笼型异步电动机复合故障下的电感参数,推导了复合故障在定子电流中产生的特定谐波频率计算公式,然后建立了笼型异步电动机正常状态和复合故障时的有限元仿真模型,通过仿真对理论结果进行了验证。结果表明,复合故障发生时定子电流中除存在单一断条和静态偏心故障的特征频率外,还会产生一系列附加谐波分量,理论分析与仿真结果相符。     

鼠笼式异步电动机转子故障检测方法

基于定子电流的频谱分析方法是异步电动机状态监测与故障诊断的主要途径。总结了异步电动机转子断条和偏心故障时，定子电流中可能出现的故障特征频率成分，通过对同时存在断条和偏心故障的异步电动机的实验结果分析，发现基波周围的断条和偏心特征频率成分尽管容易出现频率重叠，但因其幅值较大，对其进行监测仍是故障诊断最有效的途径；避免误诊的方法是对断条和偏心特征频率成分各自的分布规律进行监测分析，如果同时结合槽谐波及其周围的偏心特征频率成分的监测，将会使结果更准确。     

基于多回路数学模型的异步电动机内部故障瞬变过程

基于多回路理论,建立了YN接、Y接、D接笼型异步电动机在正常、内部故障(定子绕组匝间短路与转子断条)情况下的多回路数学模型。应用多回路数学模型对电机内部故障瞬变过程进行数字仿真和实验,以此研究电机定子电流及其负序分量、相位对称关系、边频分量的瞬变规律。分析仿真与实验结果表明：由于实际电机本身固有的不对称性,即使其正常运行,也必将在一定程度上体现内部故障特征;某些形式的定子绕组匝间短路故障可能抵消这种不对称性,并削弱(而非强化)定子绕组匝间短路故障特征;实际电机定子电流往往包含包括转子断条故障特征分量在内的多个边频分量,其中某些边频分量可能来源于电机气隙偏心、转子不对中或其他因素。在工程实际中进行电机内部故障检测时,必须密切关注上述规律,以保证故障检测灵敏度与可靠性。     

线间补偿型匝间短路故障自动容错永磁同步电机故障位置检测方法

定子绕组匝间短路故障是永磁同步电机(PMSM)最常见的故障之一。该故障会造成三相电流不平衡,输出转矩剧烈波动,输出能力下降。故障严重时,过大的短路电流会烧毁绕组。为了解决匝间短路故障产生的问题,课题组此前提出了一种具有匝间短路故障自动容错能力的PMSM。针对该特种电机,提出了一种匝间短路故障位置的检测方法。介绍了电机的特殊结构,并通过数学模型推导出利用电机漏磁路特性和原有定子线圈判别故障线圈所在相的方法。使用ANSYS软件建立电机有限元模型,对电机不同匝间短路情况进行仿真,验证了该检测方法的正确性。     

Fault classification and fault signature production for rolling element bearings in electric machines

Most condition monitoring techniques for rolling element bearings are designed to detect the four characteristic fault frequencies. This has lead to the common practice of categorizing bearing faults according to fault location (i.e., inner race, outer race, ball, or cage fault). While the ability to detect the four characteristic fault frequencies is necessary, this approach neglects another important class of faults that arise in many industrial settings. This research introduces the notion of categorizing bearing faults as either single-point defects or generalized roughness. These classes separate bearing faults according to the fault signatures that are produced rather than by the physical location of the fault. Specifically, single-point defects produce the four predictable characteristic fault frequencies while faults categorized as generalized roughness produce unpredictable broadband changes in the machine vibration and stator current. Experimental results are provided from bearings failed in situ via a shaft current. These results illustrate the unpredictable and broadband nature of the effects produced by generalized roughness bearing faults. This issue is significant because a successful bearing condition monitoring scheme must be able to reliably detect both classes of faults.     

利用逆同步速坐标变换检测感应电机定子故障

针对感应电机电流中谐波和噪声影响定子匝间短路故障诊断结果的问题,提出了利用逆同步速坐标变换提取定子故障特征的方法.利用改进的相关算法准确提取三相定子电流基波信息,通过逆同步速坐标变换将基波正序分量转换成二倍频交流量,负序分量转换成直流量.采用均值法提取直流分量,导出了负序分量在逆同步速旋转坐标系下合成矢量幅值,并考虑电机先天不平衡因素定义了表征匝间短路故障程度的灵敏度因子.实验结果表明,该方法避开了先天不平衡、谐波及噪声对诊断结果的影响,灵敏度因子可准确反映定子匝间短路故障特征.系统只需采样电压频率和电流信号,易于工程实现.     

异步电动机定子绕组的故障诊断方法

为及时发现并排除电动机的早期故障,保障电机设备的安全运行,提出利用失电残余电压诊断异步电动机定子绕组匝间短路故障的新方法并讨论了诊断匝间短路故障多种特征量的有效性;分析电动机失电后的残余电压并讨论了完好电机与故障电机残压中的频率成分。通过分析残压中高次谐波成分的变化可诊断定子绕组匝间短路故障;同时根据匝间短路故障后,完好相与故障相端电压所含谐波成分的不同,可确定故障发生的位置。实验证明该法对供电电源不对称及负载的波动具有鲁棒性,同时可诊断绕组早期轻微匝间短路故障。     

基于对称分量法的调相机定子故障特征分析

传统同步调相机定子绕组匝间短路故障机理分析方法通常认为电机定子电流接近三相对称,并以此为基础建立同步调相机故障电流的数学表征。但是,一旦发生定子绕组匝间短路故障,同步调相机三相定子电流的对称性将遭到破坏,使得传统故障机理分析方法所建立的数学表征无法准确反映电机内部电气量的变化。该文通过引入对称分量法,建立了故障后同步调相机瞬时有功功率与无功功率的数学模型,提出利用瞬时有功/无功功率中的二次谐波进行定子绕组匝间短路故障诊断。仿真和实验的结果表明：相较于利用定子电流三次谐波与基波幅值之比诊断故障的传统方法,所提方法在轻微故障状态下能提高至少7倍的诊断灵敏度,更易完成早期故障诊断。同时,所提方法中的故障特征量不受同步调相机工况和故障位置的影响,具有强鲁棒性。     

基于小波变换的异步电机故障检测与分析

通过合理选取小波基,对提取到的电机振动信号和定子电流信号做小波包变换,从而对电机滚动轴承损坏、转子断条这两类电机主要故障做出准确的判断与分析。对转子断条故障的仿真结果表明,小波包分析方法在电机故障诊断方面具有较好的准确性和优越性。