Fault detection and diagnosis of permanent-magnetic DC motors based on current analysis and BP neural networks

In order to guarantee quality during mass serial production of motors, a convenient approach on how to detect and diagnose the faults of a permanent-magnetic DC motor based on armature current analysis and BP neural networks was presented in this paper. The fault feature vector was directly established by analyzing the armature current. Fault features were extracted from the current using various signal processing methods including Fourier analysis, wavelet analysis and statistical methods. Then an advanced BP neural network was used to finish decision-making and separate fault patterns. Finally, the accuracy of the method in this paper was verified by analyzing the mechanism of faults theoretically. The consistency between the experimental results and the theoretical analysis shows that four kinds of representative faults of low power permanent-magnetic DC motors can be diagnosed conveniently by this method. These four faults are brush fray, open circuit of components, open weld of components and short circuit between armature coils. This method needs fewer hardware instruments than the conventional method and whole procedures can be accomplished by several software packages developed in this paper.

Testing and Analysis of Induction Motor Electrical Faults Using Current Signature Analysis

The proposed method deals with the emerging technique called as Motor Current Signature Analysis (MCSA) to diagnosis the stator faults of Induction Motors. The performance of the proposed method deals with the emerging technique called as Motor Current Signature Analysis (MCSA) and the Zero-Sequence Voltage Component (ZSVC) to diagnose the stator faults of Induction Motors. The unalleviated study of the robustness of the industrial appliances is obligatory to verdict the fault of the machines at precipitate stages and thwart the machine from brutal damage. For all kinds of industry, a machine failure escorts to a diminution in production and cost increases. The Motor Current Signature Analysis (MCSA) is referred as the most predominant way to diagnose the faults of electrical machines. Since the detailed analysis of the current spectrum, the method will portray the typical fault state. This paper aims to present dissimilar stator faults which are classified under electrical faults using MCSA and the comparison of simulation and hardware results. The magnitude of these fault harmonics analyzes in detail by means of Finite-Element Method (FEM). The anticipated method can effectively perceive the trivial changes too during the operation of the motor and it shows in the results.

Effect of Motor Relearning Combined with t DCS on Motor Function of Lower Extremities and Gait in Patients with Cerebral Infarction and 3D Gait Analysis

[Objectives]To observe the effect of motor relearning combined with transcranial direct current stimulation on the motor function of lower extremities in patients with cerebral infarction,and to observe its effect on gait by 3D gait analysis.[Methods]60 patients with cerebral infarction who met the inclusion criteria were randomly divided into 3 groups according to the order of treatment(n=20).Group A received motor relearning treatment,group B received transcranial direct current stimulation treatment,group C received motor relearning combined with transcranial direct current stimulation,and the curative effect was observed after 5 courses of treatment.[Results]Before treatment,FMA,MBI,spatio-temporal parameters for 3D gait analysis(gait frequency,gait cycle,stride length,gait speed,stride length deviation,double support)and lower limb joint motion parameters(affected side stride length,maximum hip flexion,maximum hip extension,maximum knee flexion,maximum knee extension,stance phase,swing phase)were compared among the three groups.After treatment,the FMA and MBI of the three groups increased,and the spatio-temporal parameters for 3D gait analysis(gait frequency,gait cycle,gait speed,double support)and the lower limb joint motion parameters(affected side stride length,maximum hip flexion,maximum hip extension,maximum knee flexion,swing phase)were all improved,while the spatio-temporal parameters(stride length and stride length deviation)and the lower limb joint motion parameters(maximum knee extension and stance phase)decreased.Compared with those before treatment,there were significant differences among the three groups(P<0.05).Through the comparison between groups,it was found that the FMA,MBI,spatio-temporal parameters for 3D gait analysis(gait frequency,gait cycle,gait speed,double support)and lower limb joint motion parameters(affected side stride length,maximum hip flexion,maximum hip extension,maximum knee flexion,swing phase)in group C were significantly higher than those in group A and B,while the spatio-temporal parameters(stride length and stride length deviation)and lower limb joint motion parameters(maximum knee extension and stance phase)in group C were significantly lower than those in group A and group B,and the difference was statistically significant(P<0.05).[Conclusions]Motor relearning combined with transcranial direct current stimulation could increase MBI and FMA,improve gait spatio-temporal parameters and lower limb joint motion parameters,and correct abnormal gait in patients with cerebral infarction.

Industrial Fault Signals Propagation and Current Signature Analysis

Motor current signature analysis provides good results in laboratory environment. In real life situation, electrical machines usually share voltage and current from common terminals and would easily influence each other. This will result in considerable amount of interferences among motors and doubt in identity of fault signals. Therefore, estimating the mutual influence of motors will help identifying the original signal from the environmental noise. This research aims at modelling the propagation of signals that are caused by faults of induction motors in power networks. Estimating the propagation pattern of fault signal leads to a method to discriminate and identify the original source of major events in industrial networks. Simulation results show that source of fault could be identified using this approach with a higher certainty than anticipated output coming of any individual diagnosis.

Dynamics modeling and electromechanical coupling characteristics analysis of cage induction motors

Induction motors, as typical electromechanical energy conversion devices, have received limited attention in previous studies on electromechanical coupling vibrations, precise modeling, and the use of electromechanical coupling effects for fault diagnosis and condition assessment in motor drive systems. This study proposes a comprehensive model of cage induction motors that integrates the multiple coupled circuit model with a rotor-bearing dynamics model. The model accounts for the linear increase in the magnetomotive force across the slot and incorporates the skidding characteristics of bearings in the rotor-bearing system. By calculating the time-varying mutual inductance parameters based on the air-gap distribution in the vibration environment, the electromechanical coupling vibration of the cage motor is investigated. Furthermore, this study examines the electromechanical coupling vibration characteristics influenced by various factors, including bearing clearances, radial loads, and the vertical excitation frequencies of the stators. The results show that the proposed model improves the excitation inputs for the electrical and mechanical systems of the motor compared with conventional models. Increased bearing clearance and radial load affect the current and torque similarly but have opposite effects on the slip ratio. This study provides a deeper understanding of electromechanical coupling mechanisms and facilitates the use of such phenomena for fault diagnosis and condition assessment in motor-driven systems.

应用AT IV对直流电动机绕组的MCA分析判别方法

对直流电动机绕组故障的监测一直是电机监测工作的一个难题。与测量一般交流电机不同,测量直流电动机时,没有可供对比的多相绕组,因此分析判断测量数据变得比较困难。文章基于MCA(电动机电路分析)技术的实际运用经验,针对各种不同接法的直流电动机,以海军当前通用的AT IV测量仪器为例,提出了比较有效的监测分析方法和判别标准,基本可以满足对直流电动机绕组故障监测诊断的一般要求。

基于Ansys和Motor-CAD对交流伺服电动机性能的研究

基于某型号导弹发射装置电机的设计指标要求,运用Ansys有限元分析软件及Motor-CAD热分析软件分别进行性能优化分析,得出合理的设计方案。通过实测数据的验证得出:方案设计的准确率可达到93.5%以上,电机表面温升最大为19.2 K,与仿真值接近,方案设计合理。

Influence of Induction Motor Design Data on Effects of Cage Asymmetry

The purpose of this paper is to analyze influence of design data on a component （1 - 2s）~ in stator currents of induction motors, mainly used for cage fault diagnosis. This paper shows that such an approach does not always lead to a correct outcome. The considerations are based on a ＂classical＂ model of induction motors extended to cage asymmetry by introducing cage asymmetry factors ko~ and ka. It has been found that in order to estimate the level of the component （1 - 2s）7~, it is enough to know the pole-pair number ＂p＂ and the number of rotor slots ＂N＂. The main objective of the paper is to provide engineers with simple qualitative prediction of effects due to cage faults for various motors when information on design data is very limited.

永磁同步电机电流环时变时滞相关稳定性分析

为探究时变时滞对永磁同步电机电流环控制系统的影响,建立了具有时变时滞的永磁同步电机电流环闭环控制系统模型,利用时滞分割法构造了Lyapunov-Krasovskii泛函,获得了保证系统内部渐进稳定的时变时滞相关稳定性判据,建立了控制器增益、时变时滞上界与闭环系统稳定性之间的关系,为实际工程应用中控制器参数设计提供了理论依据。仿真结果表明所提出的方法能够保证系统内部渐进稳定,且相较于经典方法具有更小的保守性,可为控制系统时变时滞相关稳定性分析及控制器设计提供理论支持。

经颅直流电刺激不同靶点治疗帕金森病效果的网状Meta分析

目的:系统评价经颅直流电刺激对帕金森患者运动功能的康复疗效,并比较经颅直流电刺激作用于不同靶点对帕金森患者运动功能的疗效差异,为临床中经颅直流电刺激的靶点选择提供理论依据。方法:计算机检索Cochrane Library、PubMed、Web of Science、中国知网、维普和万方数据库,以“帕金森、经颅直流电刺激”为中文检索词,以“Parkinson,transcranial direct current stimulation”为英文检索词,收集从各数据库建库至2023年1月发表的关于经颅直流电刺激改善帕金森患者运动功能的随机对照试验。使用Cochrane 5.1.0偏倚风险评估工具和PEDro量表对纳入研究进行质量评价。采用RevMan 5.4和Stata 17.0软件对结局指标进行Meta分析。结果:①最终纳入15项随机对照试验,PEDro量表评估显示均为高质量或极高质量研究。②Meta分析显示,与对照组相比经颅直流电刺激可显著提高UPDRS-Ⅲ评分(MD=-2.49,95%CI:-4.42至-0.55,P<0.05)、步频评分(MD=0.07,95%CI:0.03-0.11,P<0.05)和步速评分(MD=0.02,95%CI:0.00-0.05,P<0.05),但对BBS评分(MD=2.57,95%CI:-0.74-5.87,P>0.05)的提高不明显。③网状Meta分析概率排序结果显示,在UPDRS-Ⅲ评分方面,刺激靶点疗效的概率排序结果为背外侧前额叶皮质(52.4%)>初级皮质运动区(45.8%)>大脑中央点(1.8%)>常规康复治疗(0%);在步频评分方面,刺激靶点疗效的概率排序结果为小脑(50.1%)>大脑中央点(45.8%)>背外侧前额叶皮质(3.9%)>初级皮质运动区(0.2%)>常规康复治疗(0%);在步速评分方面,刺激靶点疗效的概率排序结果为小脑(64.8%)>背外侧前额叶皮质(23.8%)>大脑中央点(9.4%)>初级皮质运动区(1.7%)>常规康复治疗(0.4%);在BBS评分方面,刺激靶点疗效的概率排序结果为:小脑(77.4%)>背外侧前额叶皮质(20.7%)>大脑中央点(0.7%)>常规康复治疗(0.2%)。结论:经颅直流电刺激可显著改善帕金森患者运动功能,其中刺激背外侧前额叶皮质区域对改善帕金森患者运动协调方面疗效更佳,而刺激小脑区域对改善帕金森患者步行和平衡方面疗效更佳。

不同坐标系下六相PMSM单相开路容错MPC控制

目的目前六相永磁同步电机单相开路故障的模型预测容错控制的研究已逐步成为热点,本文将对α-β和d-q2种坐标系控制下的故障机理进行对比分析,并对比不同坐标系中下正常和故障容错运行模型的控制效果。方法基于矢量空间解耦坐标变换矩阵不变原理,对A相开路进行故障模型的理论计算分析,分别在α-β和d-q这2种不同坐标系中对其进行模型预测控制容错建模。最后在MATLAB/Simulink中对2种坐标系下的电机正常运行和故障容错运行中的工作性能采用相同电机参数进行实时仿真。结果仿真结果显示,正常运行时,2种坐标系下总谐波失真(THD)值分别为2.09%和2.77%;故障运行时,d-q坐标系下的THD值比α-β坐标系小了13.15%;容错运行时2种坐标系下的THD值分别为1.19%和1.79%。结论从仿真结果可以看出,d-q坐标系控制下的电机在故障时具有更稳定的性能,而在正常和容错运行状态下,2种坐标系下的控制效果几乎等效。

基于电流特征分析的五相电机驱动系统单相故障诊断仿真实验

随着仿真软件的快速发展,虚拟仿真实验已成为高校教学中的重要组成部分。为了提高学生的系统建模分析能力,实现电机驱动系统的安全可靠运行,针对五相永磁同步电机(FPMSM)驱动系统单相开路故障问题,提出了一种基于电流特征分析的故障诊断定位方法,并利用MATLAB/Simulink仿真软件进行验证。首先,研究根据故障前后三次谐波平面电流的不同表现特征,依次使用有效值计算、均方根等方法处理三次谐波平面电流,提取故障信号。其次,利用反正切函数求取的故障相位角以及滑动采样获取的符号信息进行故障定位、隔离,克服因谐波污染造成的诊断短时失效问题。最后,搭建Simulink仿真模型并进行相应分析。结果表明,所提的故障诊断算法能够快速准确地实现FPMSM驱动系统单相开路故障诊断,避免因故障长时间存在而产生的系统二次损伤及生产事故问题,为后续容错操作及故障后系统持续稳定运行奠定基础。同时,该研究为电机控制类高校虚拟教学实验提供了一个潜在案例,具有较高的理论与实用价值。

分数极路比绕组形式对可变速抽蓄电机电磁激振力的影响

可变速抽蓄电机定子分数极路比绕组可以解决机组转速、容量、出口电压不匹配问题,但不同的绕组排布也会导致各支路的磁动势不对称,从而影响电机的电磁激振力。为了研究分数极路比绕组形式对可变速抽蓄电机电磁激振力的影响,推导了适用于不同分数极路比绕组形式的可变速抽蓄电机定子磁动势和电磁激振力的解析计算公式,以336 MW的可变速抽蓄电机为例,综合考虑磁动势不对称度、磁动势幅值、相位偏差和短距系数,获得不同分数极路比绕组形式对磁动势和电磁激振力的影响规律,进而建立了基于有限元的可变速抽蓄电机电磁激振力模型,计算了不同分数极路比绕组形式下可变速抽蓄电机电磁激振力,并进一步研究了支路环流对电磁激振力的影响。结果表明,合理的分数极路比绕组排布能有效削弱电磁激振力,且采用节距为14-15-16的不等节距分数极路比绕组方案能最小化环流的影响。研究结果可为可变速抽蓄电机的国产化研制提供技术支持。

基于频域分析的PMSM双闭环PI控制器整定方法

以永磁同步电机(PMSM)双闭环调速系统为对象,在对其数学模型进行合理简化的基础上,综合考虑了逆变器开关周期、电流和速度滤波等环节的影响,给出了一种在频域进行PI控制器参数设计的整定方法。通过建立双环的数学模型,得到关于开环频域特征量的解析式,根据电流环在中低频段的特性以及转速环中阻尼系数难以精确测定的问题,分析推导了电流环和转速环PI参数简化设计的方法。利用工程经验将闭环时域指标转化开环频域特征量,并结合各类约束条件合理界定其取值范围,由此得到的PI控制器可以兼顾快速性和稳态跟踪能力。仿真和实物实验验证了整定方法的可行性,该方法确立了双环控制器参数与其对应的开环截止频率和相位裕度的解析式,并明确了开环频域特征量与闭环时域指标的关系,具有较强的工程参考和应用价值。

泵用电驱动系统EMC问题造成系统母线电流跳变分析与思考

本文针对航空航天某型泵用动力系统在器上出现母线电流异常跳变现象进行了分析,对控制器中驱动模块限流保护电路异常动作的机理进行分析及试验验证,确定了异常母线跳变的问题原因,并采取了措施。该驱动系统电磁兼容性问题的解决,提高了器件及产品的可靠性,并为同类问题解决提供了思路。

新型永磁无刷直流电机气隙磁密分析

针对微纳卫星飞轮用单圈磁钢无刷直流电机气隙磁密低且干扰磁密大的缺陷,提出了一种新型双圈磁钢构型的无刷直流电机。介绍了飞轮及新型无刷直流电机的结构和工作原理,基于等效面电流法建立新型无刷直流电机的数学模型,推导了气隙磁通密度公式。采用有限元法对有效气隙磁密和干扰磁密进行了磁密分析,结果表明,与传统无刷直流电机相比,新型无刷直流电机有效气隙磁密由0.45T提高到0.55T,气隙平顶宽度由54°拓宽到87°,干扰磁密由0.31T降低到0.065T,有效提升了电机的稳定性能和力矩输出性能,降低了功耗。

轴向磁场电机永磁体空载涡流损耗减小方法对比研究

减少轴向磁场电机永磁体空载涡流损耗的方法主要有:减小定子槽开口宽度、增大气隙长度、永磁体分块、使用屏蔽层和磁性槽楔等。基于轴向磁场电机的简化二维分析模型,分析了减小定子槽开口宽度和增大气隙长度、使用屏蔽层和磁性槽楔降低空载涡流损耗的效果。通过三维电磁场仿真,研究了永磁体不同分块方式对减少空载涡流损耗的效果。研究结果表明,减小定子槽开口宽度的效果最佳;虽然增加气隙长度可以显著减小涡流损耗,但永磁体用量迅速增加;永磁体分块减小涡流效果较好,且周向分块方式最好;屏蔽层起反作用;使用分段磁性槽楔效果比减小定子槽开口宽度稍微差一点,但加工难度要低些。

异步电动机的转子断条问题研究

讨论了异步电动机转子断条故障的特征机理,介绍了通过定子电流监测进行故障诊断的方法。概述了鼠笼式异步电动机转子断条故障可能由机械应力、过载电流和设计缺陷等因素引起,并强调了这种故障对电机性能的负面影响,包括转速异常、噪声增大和潜在的安全风险。提出了通过监测气隙磁场变化和定子电流频谱分析来诊断故障的方法。在正常情况下,电机磁动势平衡对称,而转子断条破坏了这种对称性,导致转矩波动和平均转矩下降。通过对定子电流进行频谱分析,检测到特定的谐波分量。提出结论,尽管传统理论侧重于基波的影响,实际上其他定子电流成分也参与了谐波的生成,并且可以通过监测特定频率的谐波分量来判断转子是否存在断条故障。

基于定子电流小波包分析的牵引电机轴承故障诊断

滚动轴承失效是机车牵引传动系统的主要故障源之一。轴承失效引起电机振动增加,导致定子电流发生调制。本文根据轴承失效对电机运行参数(振动、电流)的影响,将牵引电机轴承故障分为单点局部损伤和整体磨损。分析两种滚动轴承故障对振动和电机定子电流频谱的影响。定子电流分析可在不影响电机运行的情况下,检测电机的工作状况。小波包变换适应于处理瞬变、非平稳信号,用于电机定子分析能获得较高的频率分辨率,有效提取故障征兆。本文提出一种基于定子电流小波包分析的机车牵引电机轴承在线故障检测方法。HXD2型机车线路运行试验证明,该方法能有效诊断电机轴承早期故障。

基于电机定子电流的齿轮故障诊断方法

齿轮故障诊断一般采用振动信号进行故障特征提取,但振动诊断法不便于安装传感器,易受环境和噪声影响.电机本身具有传感器的特性,定子相电流等信号能够反映负载转矩的变化.因此,针对由电机驱动的齿轮传动系统,提出了一种基于电机定子相电流分析的齿轮无损故障诊断方法.推导了电机定子电流如何反应负载转矩的特性,并分析了齿轮正常与故障状态下定子电流的频谱特征,发现可通过观察边频带的出现来判断齿轮发生局部式故障.通过Matlab对故障诊断原理进行了仿真验证,在实验平台上结合频谱分析成功检测出了齿轮断齿故障.