一种负荷突变情况下异步电机转子断条故障在线检测新方法

异步电机发生转子断条故障时,其定子电流故障特征频率分量容易被电流基频淹没,加之实际工作中电机负荷突变的干扰,极大地增加了故障特征频率提取及检测的难度。为解决该问题,将解析小波和定子电流谱减法结合,提出一种有效的故障检测新方法。该方法首先利用解析小波变换来判断负荷突变点,然后通过谱减法来消除定子电流频谱中的基频分量,突出故障特征频率,进一步定义故障程度因子来量化转子断条故障程度。仿真和实验分析结果表明,该方法对于负荷突变情况下转子断条故障特征频率更加敏感,能够定量地描述转子故障程度。     

异步电机转子断条故障多重诊断方法研究

大中型异步电机多采用鼠笼型转子,而其最为常见的故障为转子断条故障。本文通过频谱分析、及park矢量图等多重化的诊断方法对这两种故障做了深入的研究,通过故障与良好状态的对比分析可以清晰的了解故障特性,为保障电机的正常运行提供比较好的理论依据。     

支持向量机在电机转子故障诊断识别中的应用

提出了一种基于支持向量机的鼠笼式电机转子断条故障检测方法,通过对电机转子断条故障进行实验模拟,获取了采样信号,利用支持向量机(SVM)对故障样本进行训练,使得支持向量机(SVM)具有分类功能.最后,采用支持向量机(SVM)对电动机各种转子断条故障进行诊断分类,取得较满意的结果.     

基于自适应陷波器的电动机转子断条故障诊断

在现代工业生产中,鼠笼式异步电动机是主要的驱动部件,其正常工作与否,直接关系到生产过程能否连续运行。鼠笼式异步电动机转子断条故障是一种隐性故障,危害比较严重,且不容易被检测到。本文提出了一种基于自适应陷波器的电动机转子断条故障诊断方法,经过仿真分析和实验证明应用自适应陷波器方法诊断电动机转子断条故障是行之有效的。     

钻机主电机转子笼条局部焊接

鼠笼式三相异步电动机鼠笼式转子的笼条运行一段时间后容易发生笼条断裂或是笼条与短路闭合端环间发生开焊常见于频繁启动的异步电机,转子笼条在电机高速运转起来在离心力的作用下被甩出后,十分容易引起定转子相卡后导致电动机定子绕组烧毁或铁芯过热退磁。进而导致昂贵的修理费用或者电机报废,损失十分巨大,了解分析转子断条开焊的故障原因后,便可提高了电机可靠性。减少生产的不必要损失。     

基于遗传神经网络的异步电动机故障诊断研究

提出一种基于遗传神经网络进行异步电机故障检测的新方法,仅利用一个振动传感器来获取异步电机的特征信息,建立电机动态非线性神经网络检测诊断模型,并利用该模型进行电机的故障检测,为减少网络权值学习搜索空间,解决神经网络权值学习中易于陷入局部最小点的问题,本文采用遗传算法实现模型权值的修正,实际使用证明利用该方法可以方便的实现在线故障诊断,且方法简单,易于实现。     

基于小波包近似熵与LMS加权特征融合异步电机故障诊断

针对大多数情况下异步电机故障在不同传感器和转频等工况参数下的近似熵集合存在差异,难以有效提取表征不同故障状态的信号特征,进行故障状态识别的问题,提出一种基于小波近似熵与加权最小均方误差LMS的特征融合异步电机故障诊断方法。首先,通过小波包分解电机正常、转子不平衡、转子弯曲以及基座松动等故障信号,得到不同频带的信号特性,然后选取最优尺度提取不同频带上近似熵构成集合。然后,结合同种故障不同运行状态下的近似熵集合,通过采用自适应LMS算法进行加权融合提取电机不同故障状态的最优特征,将其作为SVM的输入进行故障分类,从而实现不同工况下故障状态的精确识别。最后,针对异步电机正常运行、转子不平衡、转子弯曲、基座松动四种运行状态,分别采用所提出的SVM分类法和BP神经网络法,结果表明SVM分类法比BP神经网络法的分类识别率更高,诊断效果更好。     

多传感器信息融合在电机故障诊断中的应用

为了提高电机故障诊断的准确性,引入一种多传感器信息融合的诊断方法。将多个传感器所采集的转子振动频谱信号处理后,利用蚁群神经网络进行故障局部诊断,以获得彼此独立的证据,再由证据理论对各证据进行融合,最终实现对电机故障的准确诊断。实验结果表明,该方法有效提高诊断的可信度,减少电机故障分类识别的不确定性。     

基于希尔伯特变换方法检测笼型异步电动机断条故障

本文讨论了笼型异步电动机断条故障时的危害以及希尔伯特变换方法检测电机运行是否出现断条故障,同时简单叙述了希尔伯特法的优缺点。对电机断条故障检测有一定的意义。     

小波包分析在电动机转子断条故障诊断中的应用

采用改进型小波包算法从电气和机械方面提取信号的故障特征,提出一种基于小波包分析频带能量的故障诊断方法。经实验可知,该方法能够更全面的快速检测到转子断条故障的存在而且准确率高,在许多工程实践中可以应用。     

Detection of outer raceway bearing defects in small induction motors using stator current analysis

We investigate the application of induction motor stator current spectral analysis (MCSA) for detection of rolling element bearing damage from the outer raceway. In this work, MCSA and vibration analysis are applied to induction motor to detect outer raceway defects in faulty bearings. Data acquisition, recording, and fast fourier transform (FFT) algorithms are done by using the Lab VIEW programming language. Experimental results verify the relationship between vibration analysis and MCSA, and identify the presence of outer raceway bearing defects in induction machines. This work also indicates that detecting fault frequencies by motor currents is more difficult than detecting them by vibration analysis. The use of intensive resolution FFT is recommended in MCSA for detecting faults easily. Reinstalling a faulty bearing can alter the characteristic frequencies and it is difficult to compare results from different bearings or even from the same bearing in different installations.     

Detection of bearing defects in three-phase induction motors using Park’s transform and radial basis function neural networks

This paper investigates the application of induction motor stator current signature analysis (MCSA) using Park’s transform for the detection of rolling element bearing damages in three-phase induction motor. The paper first discusses bearing faults and Park’s transform, and then gives a brief overview of the radial basis function (RBF) neural networks algorithm. Finally, system information and the experimental results are presented. Data acquisition and Park’s transform algorithm are achieved by using LabVIEW and the neural network algorithm is achieved by using MATLAB programming language. Experimental results show that it is possible to detect bearing damage in induction motors using an ANN algorithm.     

Effect of Magnetic Saturation on Static and Mixed Eccentricity Fault Diagnosis in Induction Motor

We have investigated the effect of magnetic saturation of core materials on the diagnosis of static and mixed-eccentricity faults in induction motors. We modeled the faults by using a modified winding function (MWF) and time-stepping finite-element (TSFE) methods to compute the stator currents of both healthy and faulty motors for processing. We then analyzed the stator signal spectra of the motors by the MWF and TSFE methods and estimated the amplitudes of sideband components attributable to the faults. The results obtained by TSFE agreed well with experimental measurements. However, there was considerable discrepancy between the MWF results and the experimental measurements. We investigated the reason for the discrepancy by analyzing the air gap magnetic field distribution in both healthy and faulty induction motors in order to determine their linear and actual magnetization characteristics. We found that, in a faulty motor, for fixed permeability, the analytic method yields a much larger magnetic flux amplitude than is actually the case. At the same time, the distribution of magnetic flux in the air gap is more asymmetric than the actual case. Here, we present our experimental results and those obtained with the MWF method, using the finite-element analysis package Opera2d 10.5, for two three-phase, four-pole, 60-Hz, 3-hp motors having 36 stator slots and 44 and 28 rotor slots, respectively.      

FPGA-Based Multiple-Channel Vibration Analyzer for Industrial Applications in Induction Motor Failure Detection

Early detection of failures in equipment is one of the most important concerns to industry. Many techniques have been developed for early failure detection in induction motors. There is the necessity of low-cost instrumentation for online multichannel measurement and analysis of vibration in the frequency domain, and this could be fixed to the machine for continuous monitoring to provide a reliable continuous diagnosis without needing trained staff. Field-programmable gate arrays (FPGAs) are distinguished by being very fast and highly reconfigurable devices, allowing the development of scalable parallel architectures for multichannel analysis without changing the internal hardware. The novelty of this work is the development of a low-cost FPGA based on a multichannel vibration analyzer; this is capable of providing an automatic diagnosis of the motor state carrying out online continuous monitoring. To test the functionality of the proposed vibration analyzer, three experiments on 746-W (1-hp) induction motors were carried out. Such experiments are intended to detect motor failures such as broken bars, unbalance, and looseness. The obtained results show the overall system performance.      

基于HJI方法的感应电动机运行故障检测

运用鲁棒故障检测设计理论中的HJI(Hamilton-Jacobi-lssacs)不等式方法对感应电动机运行故障进行故障检测设计.文中给出了感应电动机的动态数学模型并对其进行扩展,构成了广义系统.在此基础上,使用鲁棒检测理论中的HJI不等式对运行故障进行了理论分析和推导,得出了用于进行故障检测的递推公式.通过对电机定子绕组、径向振动以及瞬时功率等方面的仿真实验研究表明,使用这种检测方法可以提高检测的鲁棒性,避免误检,同时满足各项性能指标的要求,而且检测结果较传统方法有较大提高.     

Fast Analytical Determination of Aligned and Unaligned Flux Linkage in Switched Reluctance Motors Based on a Magnetic Circuit Model

The main advantages of the switched reluctance motor are high torque, wide speed range, simple structure and fault tolerance. Because a switched reluctance motor has inherently nonlinear magnetic characteristics and a doubly salient pole structure, a finite-element analysis approach (FEA) is often adopted to obtain accurate magnetic representation. However, the solution time can be large for a FEA simulation if the mesh is detailed and/or many simulations are required. We propose a rapid analytical solution for determining the aligned and unaligned flux linkage using a magnetic circuit model. We present a simple method for obtaining the air-gap permeance for unaligned linkage. The results of our method agree well with FEA solutions.      

Decoupling features for diagnosis of reversing and check valve faults in heat pumps

Recently, a decoupling-based (DB) fault detection and diagnosis (FDD) method was developed for diagnosing multiple-simultaneous faults in air conditioners (AC) and was shown to have very good performance. The method relies on identifying diagnostic features that are decoupled (i.e., insensitive) to other faults and operating conditions. The current paper extends the DB FDD methodology to heat pumps. Heat pumps have all the same faults as occur for air conditioners with additional faults associated with components that accommodate heating mode, including reversing valve leakage and check valve leakage. Decoupling features were developed for these additional faults and laboratory evaluations were performed to evaluate diagnostic performance. It was found that check valve leakage could be detected and diagnosed before the heating capacity degradation reached 5% for a system with a fixed orifice expansion (FXO) device and 3% for the same system retrofit with a thermal expansion valve (TXV). Furthermore, the feature for check valve leakage is very insensitive to other faults and operating conditions. The decoupling feature for reversing valve leakage could successfully detect and diagnose faults for a TXV system before the heating capacity degraded 6% and was also insensitive to other faults and operating conditions. However, this feature did not work well for a system with an FXO in heating mode because the refrigerant exiting the evaporator and entering the reversing valve was typically a two-phase mixture. Fortunately, it was possible to diagnose this particular fault at many operating conditions in cooling mode for the system with an FXO.     

基于样本熵的风力发电机早期故障检测

针对发电机定子匝间短路和转子断条等早期故障特征具有幅值小、非稳态、易受工况影响等特点,引入样本熵算法实现风力发电机定子电流和电磁转矩信号特征提取,并模拟不同负载条件下故障信号,实现定量参数分析。分析结果表明,样本熵算法适用于在变工况及噪声干扰条件下,对短数据参量进行分析并实现故障特征定量描述,可用于风力发电机早期故障检测和实时在线监测。