Guaranteeing the Fault Transient Performance of Aerospace Multiphase Permanent Magnet Motor System: an Adaptive Robust Speed Control Approach

To enhance the fault transient performance of aerospace multiphase permanent magnet synchronous motor(PMSM)system,an adaptive robust speed control is proposed regardless of the phase open-circuit(OC)and short-circuit(SC)fault in this paper,which can be applied for both the redundant motor system and fault tolerant motor system.For aerospace multiphase PMSM system,besides external load disturbance and system parameter perturbation,there inevitably exists the electromagnetic torque ripple in fault transient process,which can degrade the system performance and even cause the system instability.To cope with this issue,the electromagnet torque ripple of the multiphase PMSM system in fault transient process is first analyzed.Then,by considering the electromagnet torque fluctuation caused by fault transient as a system uncertainty,a novel adaptive robust speed control scheme is proposed,while the adaptive law is constructed to emulate the total system uncertainty bound,which include the load disturbance,the parameter variation,and the electromagnetic torque fluctuation due to fault transient.The resulting control can ensure the speed control performance even in fault transient process regardless of the uncertainty,in which no prior estimation of the uncertainty bound is required.In addition,the proposed adaptive robust speed control is demonstrated by a six-phase PMSM experimental platform.The novelty of this research is to explore a novel adaptive robust speed control to strengthen the fault tolerance performance of multiphase PMSM system even in fault transient process,which requires no prior estimation of the uncertainty bound.

Rule-based Fault Diagnosis of Hall Sensors and Fault-tolerant Control of PMSM

Hall sensor is widely used for estimating rotor phase of permanent magnet synchronous motor(PMSM). And rotor position is an essential parameter of PMSM control algorithm, hence it is very dangerous if Hall senor faults occur. But there is scarcely any research focusing on fault diagnosis and fault-tolerant control of Hall sensor used in PMSM. From this standpoint, the Hall sensor faults which may occur during the PMSM operating are theoretically analyzed. According to the analysis results, the fault diagnosis algorithm of Hall sensor, which is based on three rules, is proposed to classify the fault phenomena accurately. The rotor phase estimation algorithms, based on one or two Hall sensor(s), are initialized to engender the fault-tolerant control algorithm. The fault diagnosis algorithm can detect 60 Hall fault phenomena in total as well as all detections can be fulfilled in 1/138 rotor rotation period. The fault-tolerant control algorithm can achieve a smooth torque production which means the same control effect as normal control mode (with three Hall sensors). Finally, the PMSM bench test verifies the accuracy and rapidity of fault diagnosis and fault-tolerant control strategies. The fault diagnosis algorithm can detect all Hall sensor faults promptly and fault-tolerant control algorithm allows the PMSM to face failure conditions of one or two Hall sensor(s). In addition, the transitions between health-control and fault-tolerant control conditions are smooth without any additional noise and harshness. Proposed algorithms can deal with the Hall sensor faults of PMSM in real applications, and can be provided to realize the fault diagnosis and fault-tolerant control of PMSM.

Visualization for Explanation of Deep Learning-Based Fault Diagnosis Model Using Class Activation Map

Permanent magnet synchronous motor(PMSM)is widely used in various production processes because of its high efficiency,fast reaction time,and high power density.With the continuous promotion of new energy vehicles,timely detection of PMSM faults can significantly reduce the accident rate of new energy vehicles,further enhance consumers’trust in their safety,and thus promote their popularity.Existing fault diagnosis methods based on deep learning can only distinguish different PMSM faults and cannot interpret and analyze them.Convolutional neural networks(CNN)show remarkable accuracy in image data analysis.However,due to the“black box”problem in deep learning models,the diagnostic results regarding providing accurate information to the user are uncertain.This paper proposes a motor fault diagnosis method based on improved deep residual network(ResNet)and gradient-weighted class activation mapping(Grad-CAM)to analyze demagnetization and eccentricity faults of permanent magnet synchronous motors,and the uncertainty limitation of fault diagnosis based on the convolutional neural network is overcome by the visual interpretation method.The improved ResNet is formed by using ResNet9 as the backbone network,replacing the last convolution layer with a atrous spatial pyramid pooling(ASPP),and adding a multi-scale feature fusion module and attention channel mechanism(CAM).The proposed model not only retains the effective extraction of image features by ResNet9 but also enhances the sensitivity field of the network through the hollow convolution pyramid and realizes the feature fusion of the web on different scales through the multi-scale feature fusion module(MSFFM),further improving the diagnostic accuracy of the network on different types of fault features.The diagnostic effect of the network is verified on the selfmade data set,which mainly includes five states:normal(He),25%demagnetization(De25),50%demagnetization(De50),10%static eccentricity(Se10),and 20%static eccentricity(Se20).The number of pictures in the training set is 6000,and the number in the test set is 1500.The average diagnostic accuracy of the improved ResNet on this dataset is 99.00%,which is 1.04%,8.89%,4.58%,and 7.22%higher than that of the multi-column convolutional neural network(MCNN),Bi-directional long short-term memory(Bi-LSTM),deep belief network(DBN),and recurrent neural network(RNN)models,respectively.Finally,gradient activation heat maps were used to globally average pool the final output feature map of the network to obtain feature weights.They were superimposed with the original image to get gradient activation heat maps of different grayscale images.The warmer the tone of the heat map,the greater the impact on the network diagnosis results,and then the demagnetization and eccentricity fault characteristics of the permanent magnet synchronous motor were determined-visual characterization of quantitative analysis.

Method for Fault Diagnosis and Speed Control of PMSM

In the field of fault tolerance estimation,the increasing attention in electrical motors is the fault detection and diagnosis.The tasks performed by these machines are progressively complex and the enhancements are likewise looked for in the field of fault diagnosis.It has now turned out to be essential to diagnose faults at their very inception;as unscheduled machine downtime can upset deadlines and cause heavy financial burden.In this paper,fault diagnosis and speed control of permanent magnet synchronous motor(PMSM)is proposed.Elman Neural Network(ENN)is used to diagnose the fault of permanent magnet synchronous motor.Both the fault location and fault severity are considered.In this,eccentricity fault may occur in the motor.To control the speed of the permanent magnet synchronous motor,Dolphin Swarm Optimization(DSO)algorithm is used.The proposed work is simulated by using MATLAB in terms of amplitude,speed and torque.The comparison graph of speed vs.torque obtained by the proposed method gives better result compared to the other existing techniques.The proposed work is also compared with Particle Swarm Optimization(PSO)and Elephant Herding Optimization(EHO)algorithm.The proposed usage of Elman Neural Network to detect the fault and the usage of Dolphin Swarm Optimization algorithm to control the speed of the permanent magnet synchronous motor gives better outcome.

基于简化有限集模型预测电流控制的五相PMSM统一容错控制

在五相永磁同步电机(permanent-magnet synchronous motor,PMSM)中,有限集模型预测容错控制(finite control set model predictive fault tolerant control,FCS-MPFTC)存在计算量大、电流谐波含量高等问题。因此,该文提出一种简化FCS-MPFTC来实现相开路和短路故障情况下的统一容错控制。首先,将模型预测电流控制的电流代价函数等效转化为电压代价函数,并采用无差拍方法通过电流模型计算出参考电压。然后,基于抑制三次谐波电流为0的原则合成虚拟电压矢量(virtual voltage vector,V^(3));通过重构V^(3)和扇区,以直接获得参考电压矢量对应的最优电压矢量。最后,对传统和简化FCS-MPFTC在开路和短路故障下进行对比实验。结果表明,所提策略能够有效减小故障后计算量、转矩脉动以及电流谐波含量。

基于径向气隙磁密和定子电流的永磁同步电机均匀退磁故障诊断研究

该文提出一种基于径向气隙磁密和定子电流的永磁同步电机均匀退磁故障诊断方法。首先,建立电机的d轴等效磁路模型,分析均匀退磁故障后径向气隙磁密的变化规律;然后,获取电机的径向气隙磁密和定子电流信号,对各极下径向气隙磁密幅值进行归一化处理,并绘制雷达图,根据雷达图和比较相同定子电流幅值下健康电机和故障电机的各极下径向气隙磁密幅值的平均值,来诊断均匀退磁故障;利用空载各极下径向气隙磁密幅值的平均值计算故障程度。最后,仿真和实验结果均验证所提均匀退磁故障诊断方法的有效性。

一种永磁同步电机模型失配容错控制算法

针对高精度永磁同步电机驱动系统因转动惯量、摩擦系数与磁链参数失配造成其控制精度和稳定性降低的问题,提出了一种基于有限时间收敛扩张状态观测器的无模型滑模容错控制方法。首先,建立了考虑内外不确定性的永磁同步电机转速环超局部模型,并设计了无模型全局积分终端滑模控制器;其次,设计了有限时间收敛扩张状态观测器来降低模型失配带来的影响;最后,通过与其他方法的仿真和实验对比,验证了所提方法能够在不获取精确模型的情况下实现了对多种模型失配与外部扰动工况的容错控制,并提高了电机的控制精度。

基于扰动观测器的五相永磁同步电机开路和短路容错矢量控制

针对反电动势含有3次谐波的五相永磁同步电机(PMSM)开路和短路故障导致转矩脉动大的问题,该文提出一种基于扰动观测器的矢量控制(DOB-FOC)策略。该策略基于降阶正交变换矩阵在同步旋转坐标系上建立PMSM开路故障情况下的基波模型,将3次谐波反电动势、短路电流等作为扰动信号。在此基础上,设计扰动观测器,观测出以上扰动导致的转矩脉动,将其转化为q轴补偿电流前馈到q轴电流指令中,有效地抑制了故障导致的转矩脉动,并且实现了PMSM在开路和短路故障情况下的平稳无扰运行。仿真和实验结果验证了所提容错策略的有效性。

永磁同步电机故障诊断方法研究综述

永磁同步电机(permanent magnet synchronous motor,PMSM)具有结构简单、运行稳定、效率高和外形多样等显著优点,在生产制造领域得到广泛应用。PMSM在工作中不断受到速度变化和负载波动等复杂工况的影响,不可避免地会出现各种故障,如轴承故障、偏心故障、退磁故障和匝间短路故障等。文中综述了PMSM常见故障类型,并总结了自动化领域中基于信号的状态监测方法的现有研究,以用于各类电气和机械故障的检测和诊断。随后,对诊断方法进行了总结,并分析了其优势与限制,探讨了不同信号处理方法在应用中的利弊。最后,根据当前研究现状,讨论了PMSM故障监测与诊断存在的问题和未来发展方向。

基于自然容错开关表的五相永磁同步电机直接转矩控制

五相永磁同步电机(permanent-magnetsynchronous motor,PMSM)传统直接转矩控制(direct torque control,DTC)存在相电流畸变严重、共模电压(common-modevoltage,CMV)高、转矩和磁链脉动大等问题,且无法实现相开路故障情况下的无扰运行。为解决上述问题,提出一种基于自然容错开关表的DTC策略。该策略根据PMSM开路故障前后基电压矢量的特点以及三次平面合成电压矢量为零的原则,构建虚拟矢量(virtual vector,VV),设计故障前后同一套自然容错开关表,进而不但抑制了故障导致的转矩脉动,而且在故障前后均能降低三次谐波电流、减小转矩和磁链脉动、抑制CMV。实验结果验证所提策略的可行性。

基于残差卷积网络的多传感器融合永磁同步电机故障诊断

作为工业生产与日常生活的常见设备,永磁同步电机的故障诊断研究具有十分重要的意义。以永磁同步电机的匝间短路、退磁、轴承故障为诊断目标,提出一种新型的多传感器特征融合网络(MSFFN),结合多传感器融合技术与卷积神经网络实现永磁同步电机的可靠故障诊断。网络采用2个带有残差模块的卷积神经网络,对输入的电流信号与振动信号并行提取隐藏特征,并设计一种中间特征融合模块(IFFM)有效融合电流和振动的各层隐藏特征,IFFM基于注意力机制对网络中的电流特征与振动特征进行筛选,自适应关注不同信号的内在相关特征,以实现更好的诊断效果。搭建了故障样机测试平台进行数据采集与实验验证,实验结果表明,提出方法具有更高的诊断准确率,同时在叠加了强噪声的条件下,具备更强的抗干扰能力。

复杂环境新能源汽车永磁电机传感器故障诊断

新能源汽车电机驱动过程中由于电机系统本身噪声、传感器运行特性和各个部件接触不良等因素影响下,永磁电机传感器易处于异常工作状态和故障状态。电动汽车电机运行环境的复杂性导致故障诊断难度也相对较大。于是提出新能源汽车永磁电机传感器故障诊断方法。通过分析永磁电机机械运动方式构建新能源汽车永磁电机数学模型,将上述模型与滑模观测器结合,捕捉任意时刻永磁电机传感器故障状态。基于此,提取传感器故障状态下的特征量,并输入支持向量机中,根据支持向量机输出的分类结果实现新能源汽车永磁电机传感器故障诊断。实验结果表明,针对新能源汽车恒速形式和新能源汽车变速形式,研究方法的诊断结构均与实际情况相符。

电动汽车永磁同步电机匝间短路检测算法仿真

当电机出现匝间短路故障时,电机内部电阻会大幅度降低,电流突增,造成其余元件功率过大问题。若不能及时监测该故障电机将会导致二次损坏。但是,由于短路故障具有瞬时性,其特征获取难度较大。为此提出电动汽车永磁同步电机匝间短路故障检测方法。构建驱动汽车的永磁同步电机模型,依据当永磁同步电机模型处于匝间短路故障状态时,基波电流与正序电流的制约关系失效的原理,提取电机匝间短路故障特征。基于此利用粒子群算法-最小二乘支持向量机(Particle swarm optimization-Least Squares Support Vector Machine,PSO-LSSVM)获取故障检测结果,实现电动汽车永磁同步电机匝间短路故障的检测。实验结果表明,研究方法在任意时刻检测到的负载力矩均与实际值吻合,且输出的电机残余能量具有较高可靠性,说明了上述方法具有较强的可应用性。

A novel fault tolerant permanent magnet synchronous motor with improved optimal torque control for aerospace application

Improving fault tolerant performance of permanent magnet synchronous motor has always been the central issue of the electrically supplied actuator for aerospace application. In this paper, a novel fault tolerant permanent magnet synchronous motor is proposed, which is character- ized by two stators and two rotors on the same shaft with a circumferential displacement of mechanical angle of 4.5°. It helps to reduce the cogging torque. Each segment of the stator and the rotor can be considered as an 8-pole/10-slot five-phase permanent magnet synchronous motor with concentrated, single-layer and alternate teeth wound winding, which enhance the fault isola- tion capacity of the motor. Furthermore, the motor has high phase inductance to restrain the short-circuit current. In addition, an improved optimal torque control strategy is proposed to make the motor work well under the open-circuit fault and short-circuit fault conditions. Simulation and experiment results show that the proposed fault tolerant motor system has excellent fault tolerant capacity, which is able to operate continuously under the third open-circuit fault and second short- circuit fault condition without system performance degradation, which was not available earlier.

电动汽车用永磁同步电机失磁机理与选区渗重稀土研究

由于电动汽车用永磁同步电机(PMSM)具有高功率密度的设计要求,使得磁钢更易受到电磁以及高温影响致使出现不可逆失磁,为此常在磁钢制备时加入大量镝、铽等贵重稀土元素,但该方法使得磁钢材料价格激增。本文以一台“三角”型内置式永磁同步电机为研究对象,分别研究了温度、电枢电流大小以及弱磁角这三个因素对电机磁钢的失磁特性影响,进而确定磁钢的易失磁位置及失磁扩散规律。在此基础上,通过将电机磁钢材料的牌号更改为较低的牌号,并提出合理的选区渗重稀土设计方案,实现了在不损失电机性能的情况下,减少重稀土元素用量的目的。最后,提出一种适用于计算选区渗重稀土设计方案中最大内禀矫顽力提升倍数的方法,可为电动汽车用永磁同步电机磁钢减重稀土研发设计提供参考。

Fault tolerant control with torque limitation based on fault mode for ten-phase permanent magnet synchronous motor

This paper proposes a novel fault tolerant control with torque limitation based on the fault mode for the ten-phase permanent magnet synchronous motor （PMSM） under various open-circuit and short-circuit fault conditions, which includes the optimal torque control and the torque limitation control based on the fault mode. The optimal torque control is adopted to guarantee the ripple-free electromagnetic torque operation for the ten-phase motor system under the post-fault condition. Furthermore, we systematically analyze the load capacity of the ten-phase motor system under different fault modes. And a torque limitation control approach based on the fault mode is proposed, which was not available earlier. This approach is able to ensure the safety operation of the faulted motor system in long operating time without causing the overheat fault. The simulation result confirms that the proposed fault tolerant control for the ten-phase motor system is able to guarantee the ripple-free electromagnetic torque and the safety operation in long operating time under the normal and fault conditions.

基于改进DTW算法的永磁同步电机失磁故障模拟与诊断

为实现永磁同步电机(permanent magnet synchronous motor,PMSM)失磁故障模拟与失磁故障程度诊断,提出一种基于改进的动态时间规整(dynamic time warping,DTW)算法的故障波形诊断方法。首先,对永磁同步电机进行机理分析和模拟,采集不同程度失磁故障下的电机转速波形数据集。其次,使用改进DTW算法拟合失磁故障程度最佳路径曲线方程。然后,根据定子电流的频率谐波分量确定电机是否发生失磁故障。最后,使用改进DTW算法对失磁故障波形数据进行故障程度诊断。实验表明,该方法对故障波形数据进行处理后,可以快速准确地计算出故障波形的故障程度,该方法具备较强的稳定性和实时性。

Research on a six-phase permanent magnet synchronous motor system at dual-redundant and fault tolerant modes in aviation application

With the development of more/all electrical aircraft technology, an electro-mechanical actuator（EMA） is more and more used in an aircraft actuation system. The motor system, as the crucial part of an EMA, usually adopts the redundancy technology or fault tolerance technology to improve the reliability. To compare the performances of these two motor systems, a 10-pole/12-slot six-phase permanent magnet synchronous motor（PMSM） is designed with the concentrated single-layer winding, which is able to operate at dual-redundant and fault tolerant modes.Furthermore, the position servo performances of the six-phase PMSM at dual-redundant and fault tolerant modes are analyzed, including the normal and fault conditions. In addition, a variable structure proportional-integral-derivative（PID） control strategy is proposed to solve the performance degradation problem caused by phase current saturation. Simulation and experimental results show that the fault tolerant PMSM has a better position servo performance than the dual-redundant PMSM, and the variable structure PID control strategy is able to improve the performance due to phase current saturation.

复杂工况下的永磁同步电机典型绕组故障在线诊断

绕组故障作为永磁同步电机常见的故障之一,严重影响电机正常运行。但由于电机运行工况复杂,故障特征波动严重,基于故障特征的诊断精度较低。为提高复杂工况下绕组故障的诊断精度,该文提出一种复杂工况下基于控制器信号的在线诊断方法。首先,对典型绕组故障进行故障机理分析,并通过基于自适应随机窗的快速傅里叶变换(FFT),提取控制器信号的相应故障特征;其次,通过研究单一工况和复杂工况下的各故障特征分布,揭示部分故障特征会在低转速工况下失效;再次,定义了复杂工况下故障特征性能指标,用于筛选故障特征;最后,在人工神经网络的基础上,提出了深度优化人工神经网络,引入批量归一化(BN)算法,并对深度网络结构残差化,提高网络泛化能力和诊断准确性。实验结果表明,通过计算故障特征性能指标,能够在诊断前对故障特征进行有效筛选,且深度优化人工神经网络的诊断准确性高、泛化能力强,在复杂工况下能够实现电机典型绕组故障的精确在线诊断。

模块化永磁直线同步电机考虑制造公差的推力鲁棒性优化

模块化永磁直线同步电机(MPMLSM)具有效率与推力密度高、可靠性和可加工性好等优点,非常适用于长行程运输系统;缺点是批量生产中性能易受到加工公差的影响。针对这一问题,该文提出了一种考虑制造公差的综合多目标鲁棒优化设计方法。首先,基于六西格玛设计方法建立了鲁棒优化模型;其次,采用拉丁超立方采样方法在尺寸公差范围内根据正态分布规律进行抽样,模拟大规模生产时电机尺寸受公差影响可能会出现的各种变化;再次,在尺寸优化范围内均匀抽样构成足够的训练样本,由有限元软件仿真这些抽样方案的推力性能,并基于反向传播神经网络建立电机设计代理模型;然后,应用该代理模型对拉丁超立方采样得到的样本进行电机性能的计算,求解样本整体对应的各优化目标的均值与方差,进而计算多目标优化的适应度;最后,采用非支配排序遗传算法Ⅱ进行全局优化,得到鲁棒优化方案,与不考虑公差的确定性优化方案相比,验证了该方法的有效性。鲁棒优化方案虽然略微增加了电机体积和推力波动,但是其失效概率低,受公差影响小,更加符合产品批量生产过程中的质量要求。