Simulation Research of Fault Model of Detecting Rotor Dynamic Eccentricity in Brushless DC Motor Based on Motor Current Signature Analysis

基于Ansoft／Maxwell设置动态偏心故障，建立求解电机电感和磁链的有限元模型，通过仿真，证明了将感应电机动态偏心故障的特征频率经过简化后，同样适用于无刷直流电动机。基于Ansoft／Simplorer建立无刷直流电动机系统的仿真模型。在电机稳态运行下，对定子电流进行傅里叶分析，研究并建立基于定子电流监测动态偏心故障的仿真模型：动态偏心故障与特征频率的关系、动态偏心故障程度与特征频率幅值的关系。进而研究了无刷直流电动机稳态运行时转速波动对偏心故障监测的影响。仿真结果表明，转子偏心程度加大，特征频率的幅值增加。

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.

Design and Optimization of Dual-Winding Fault-Tolerant Permanent Magnet Motor

To improve the performance of the traditional fault-tolerant permanent magnet(PM)motor,the design and optimal schemes of dual-winding fault-tolerant permanent magnet motor(DWFT-PMM)are proposed and investigated.In order to obtain small cogging torque ripple and inhibiting the short-circuit current,the air gap surface shape of the PM and the anti short-circuits reactance parameters are designed and optimized.According to the actual design requirements of an aircraft electrical actuation system,the parameters,finite element analysis and experimental verification of the DWFT-PMM after optimal design are presented.The research results show that the optimized DWFT-PMM owns the merits of strong magnetic isolation,physics isolation,inhibiting the short circuit current,small cogging torque ripple and high fault tolerance.

A robust principal component analysis-based approach for detection of a stator inter-turn fault in induction motors

Health condition monitoring of induction motors is important because of their vital role and wide us in a variety of industries.A stator inter-turn fault(SITF)is considered to be the most common electrical failure according to statisti-cal studies.In this paper,an algorithm for the detection of an SITF is presented.It is based on one of the blind source separation techniques called principal component analysis(PCA).The proposed algorithm uses PCA to discriminate between the faulty components of motor current signatures and motor voltage signatures from other components.The standard deviation of one of the decomposed vectors is used as a statistical SITF criterion.The proposed criterion is robust to non-fault conditions including voltage quality problems and large mechanical load changes as well as harmonic contaminants in the voltage supply.In addition,with a straightforward and low computational burden in the fault detection process,the proposed method is computationally efficient.To evaluate the performance of the proposed method,large numbers of practical and simulation scenarios are considered,and the results confrm the good performance,high degree of accuracy,and good convergence speed of the proposed method.

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.

A New Diagnostic Method for Winding Short-Circuit Fault for SRM Based on Symmetrical Component Analysis

Winding short-circuit is one of the more common faults in switched reluctance motors(SRM).This paper takes an in-depth look at winding short-circuit.The characteristic of non-sinusoidal intermittent single phase current,fundamental components are extracted to reconstruct four phase symmetrical currents based on spectrum analysis of phase currents.The method of symmetrical component is used to calculate positive and negative sequence components of reconstructed currents,where then the ratio between positive and negative sequence component is seen as a fault feature and the diagnostic criterion is proposed.The simulation and experimental results are presented to confirm the implementation of the proposed method.

Fault detection method with PCA and LDA and its application to induction motor

A feature extraction and fusion algorithm was constructed by combining principal component analysis(PCA) and linear discriminant analysis(LDA) to detect a fault state of the induction motor.After yielding a feature vector with PCA and LDA from current signal that was measured by an experiment,the reference data were used to produce matching values.In a diagnostic step,two matching values that were obtained by PCA and LDA,respectively,were combined by probability model,and a faulted signal was finally diagnosed.As the proposed diagnosis algorithm brings only merits of PCA and LDA into relief,it shows excellent performance under the noisy environment.The simulation was executed under various noisy conditions in order to demonstrate the suitability of the proposed algorithm and showed more excellent performance than the case just using conventional PCA or LDA.

Thorough Validation of a Rotor Fault Diagnosis Methodology in Laboratory and Field Soft-Started Induction Motors

Induction motors are the most widespread rotating electrical machines in industry.Predictive maintenance of the motors is of crucial importance due to the fact that unexpected faults in those machines can lead to huge economic losses for the corresponding companies.Over recent years,there is an increasing use of industrial induction motors operated by different types of drives,which have different functionalities.Among them,the use of soft-starters has proliferated due to the inherent benefits provided by these drives:they damp the high starting currents,enabling the soft startup of the motors and avoiding undesirable commutation transients introduced by other starting modalities.In spite of these advantages,they do not avoid the possible occurrence of rotor damages,one of the most common faults in this type of motors.Few works have proposed predictive maintenance techniques that are aimed to diagnose the rotor condition in soft-started machines and even fewer have demonstrated the validity of their methods in real motors.This work presents,for the first time,the massive validation of a rotor fault diagnosis methodology in soft-started induction motors.Industrial and laboratory and induction motors started under different types of soft-starters and with diverse rotor fault conditions are considered in the work.The results prove the potential of the approach for the reliable assessment of the rotor condition in such machines.

基于T-S模糊故障树的驱动电机冷却系统可靠性分析

车用驱动电机液压冷却系统存在故障机理不确定等问题。本文应用模糊数描述其故障概率和故障程度,建立驱动电机冷却系统的T-S模糊故障树模型,计算系统模糊可能性,分析各部件的概率重要度和关键重要度,找出影响系统可靠性的关键部件。

基于相位差的轴向磁通无铁心电机早期轻微匝间短路故障诊断

针对轴向磁通定子无铁心电机早期匝间短路故障问题,提出一种基于零序分量和定子电流分量相位差的轴向磁通定子无铁心电机的早期匝间短路故障诊断和定位方法。首先,根据定子绕组电感极小的特点建立了匝间短路故障数学模型;其次,对故障前后的短路电流、相电流、零序分量等进行了傅里叶分析,通过零序电压基波幅值变化对匝间短路故障进行识别;最后,通过对比零序电压基波与定子三相电流初相位差来进行故障相定位。结果表明,匝间短路故障相的相电流基波初始相位与零序电压基波初相位差的绝对值近似180°,而健康相的相位差与180°相差较大。基于相位差可以实现轴向磁通无铁心电机早期匝间短路故障的诊断与定位,为永磁电机的匝间短路故障诊断提供了参考。

基于小波包能量分析和信号融合的异步电机转子故障诊断

为提高异步电机转子故障诊断的可靠性,文中介绍了一种基于小波包能量分析和信号融合的异步电机转子故障诊断方法。采用定子电流信号和振动信号的频谱特征融合作为转子断条以及气隙偏心故障的诊断依据,首先对信号进行小波包分解,获得不同小波包频带节点下对应的能量分布,并与正常电机信号进行比较,进而对能量异常的信号频段进行小波包节点重构,最后通过快速傅里叶变换识别故障特征频率,诊断电机故障是否发生。通过仿真分析,验证了该方法的有效性和实用性,对于电机运行状态的准确监测具有重要意义。

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。

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VMD⁃ESA和IPOA⁃XGBOOST相结合的异步电机故障诊断

为了提高异步电机故障诊断的准确度,提出一种结合变分模态分解(VMD)、包络谱分析法(ESA)和改进的鹈鹕优化算法优化的极限梯度提升模型(IPOA‐XGBOOST)的智能诊断方法。首先,对实测的异步电机振动信号进行VMD分解,并用ESA计算VMD分解得到的本征模态分量(IMFs)的瞬时能量矩阵;然后用奇异值分解法(SVD)对得到的瞬时能量矩阵进行特征提取;最后,使用提取到的特征向量训练IPOA‐XGBOOST模型,得到异步电机的故障诊断准确率。另外,为了解决鹈鹕优化算法容易陷入局部最优解、寻优速度慢等问题,使用Circle映射改进鹈鹕优化算法。将改进的鹈鹕优化算法、遗传算法(GA)和鹈鹕优化算法进行寻优分析,实验结果表明,改进的鹈鹕优化算法的寻优效果最好。

浅析某型电机异常停转故障的问题

本文针对某型电机在验收试验中出现异常停转的故障进行了分析,对驱动器中功率MOSFET短路失效的机理进行分析及试验验证,确定了异常停转问题的原因,并采取了改进措施,为同类问题解决提供了可供借鉴的途径。

某风电场偏航电机抱闸烧毁故障分析及治理

该文主要对某风电场偏航电机抱闸烧毁故障进行分析,并提出相应的解决方案。通过对故障原因的分析,发现设计、制造和使用问题都可能导致抱闸烧毁,进而影响风电场的正常运行和环境安全。为解决这一问题,文章提出技术改造、维护措施和操作培训3个方面的解决方案。

炼化企业低压电动机常见起停故障分析

通过分析炼化企业中低压电动机起、停故障,列举出较为典型的几起低压电动机起、停故障事件。详细论证了低压电动机起动、停机异常情况时,故障的现象及处理过程,归纳并总结出产生故障的原因及处理方法,为电动机安全运行积累故障原因及处理的经验。从而降低电动机故障率,从源头避免装置因电动机问题引发运行异常,提高炼化企业装置运行的可靠性,保证装置安全平稳运行。

基于故障树分析法的直流牵引电机碳刷故障诊断

故障树分析法是一种图形演绎方法,反映故障事件在一定条件下发生的逻辑规律。故障树分析法把系统的故障树与组成系统各部件的故障有机地联系在一起,可以找出系统全部可能的失效状态。故障树本身也是一种形象化的技术资料,在它建成以后,对系统的管理和运行人员也起到了直观教学和维修指南的作用。为了有效降低和谐机车发生故障的概率,确保和谐机车的运营质量,文章通过对直流牵引电机碳刷断辫故障建立故障树,分析总结了直流牵引电机碳刷发生断辫故障的主要原因,为确保直流牵引电机碳刷稳定运行提供了可靠依据。

综合自动化系统在电机故障诊断中的应用

湖南华菱涟源钢铁厂属于大型钢铁冶金企业,电机类负荷使用较多,电机故障在电气设备故障中占比约80%。为及早发现电机故障、提升企业的生产效率,以两起电机故障的排查为例,分析综合自动化系统运行监控电气设备的电流、电压曲线在协助故障查找方面的优势,对电机类负荷的安全运行具有一定的借鉴意义。

人工智能和大数据分析技术在热连轧辊道电机故障分析中的应用

通过研究并应用人工智能和大数据分析技术,提高热连轧辊道电机故障分析的效率和准确性。通过对热连轧辊道电机运行情况进行分析和建模,探索一条准确有效的电机异常诊断建模路线,并引入大数据分析技术,利用大数据处理平台和相关算法,进一步提高诊断的精确性,使现场技术人员在电机运转过程能够及时发现潜在的故障问题,从而提高生产效率。