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

doi:10.19595/j.cnki.1000-6753.tces.221956

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摘要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,本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;然后,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;接着,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;其后,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,得到了鲁棒优化方案,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。

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关键词 ：模块化永磁直线同步电机（MPMLSM）, 制造公差, 鲁棒优化设计, 六西格玛设计, 代理模型

Abstract：The modular structure of modular permanent magnet linear synchronous motor (MPMLSM) is beneficial to simplify the winding manufacturing process and improve the motor performance and fault tolerance rate. As a motor with high efficiency, high thrust density and high reliability, it has received extensive attention and application in recent years. However, in mass production, the modular structure is more susceptible to machining tolerances. In order to improve the motor performance and better meet the quality requirements, this paper proposes a thrust robustness optimization method considering manufacturing tolerance, which provides help for the optimization design and application of MPMLSM.
First of all, a robust optimization model is established based on Design for Six Sigma, and the optimization objective function is determined according to the sigma level. Then, the Latin Hypercube Sampling (LHS) method is used to sample within the dimensional tolerance range according to the normal distribution law to simulate possible variations in motor dimensions in mass production. In order to calculate the motor performance required for subsequent optimization quickly and conveniently, it is necessary to establish a motor design surrogate model based on Back Propagation Neural Network (BPNN). The training samples of the model are obtained by uniform sampling, and the thrust performance of the samples is simulated by finite element software. Subsequently, the motor performance of the samples obtained by LHS is calculated by the trained surrogate model, and then the mean and variance of the whole sample are solved and substituted into the objective function to obtain the fitness value. Finally, Non-dominated Sorting Genetic Algorithm II is used for global optimization to obtain the Pareto front and the robust optimization schemes. Compared with the deterministic optimization scheme, the effectiveness of the method is verified.
Through finite element verification, the determination coefficient of the surrogate model established by BPNN is 0.9999, and the mean square error is 1.76×10^-3. Thus, the motor performance can be calculated quickly and accurately by this model. Under the harsh condition that the allowable thrust variation range (λ) is 2%, the robust optimization scheme can reduce the probability of failure (POF) from 67.32% to 7.96% under condition 1, and from 53.3% to 1.76% under condition 2. Compared with the deterministic optimization scheme without considering tolerance, it can be found that although the robust optimization design slightly increases the motor volume and thrust ripple, the robustness is improved. At the same time, it can be inferred that the POF of the robust optimization scheme is 0 when λ ≥ 7% under condition 1 or λ ≥ 5% under condition 2. The reduction of failure probability indicates that the MPMLSM robust optimization schemes have higher qualification rates in mass production and are less affected by tolerances.
Through analysis, the following conclusions can be drawn: 1) Under the premise of convergence, reducing manufacturing tolerance can effectively reduce the motor volume and make the motor thrust closer to the set value, but it will also increase the manufacturing cost. 2) λ mainly affects the thrust fluctuation and has little effect on the volume. Under the same volume condition, the thrust fluctuation increases with the increase of λ. 3) In the optimization results, the tooth height and primary polar distance vary with different tolerance conditions, while the remaining variables are optimized to optimum values. 4) The POF of the robust optimization scheme is lower than that of the deterministic optimization scheme, especially when λ is small. Therefore, the robust optimization scheme has better robustness and is more in line with the quality requirement in mass production.

Key words： Modular permanent magnet linear synchronous motor (MPMLSM) manufacture tolerance robust optimization design design for six sigma surrogate model

PACS:

TM351

基金资助:国家自然科学基金面上项目（52177061, 52107060）

通讯作者: 李焱鑫男,1988年生,副研究员,硕士生导师,研究方向为新型直线电机与永磁电机的建模分析与优化分析。E-mail：eeliyanxin@zju.edu.cn

作者简介: 龚夕霞女,1997年生,硕士,研究方向为模块化永磁直线同步电机设计与优化。E-mail：gongxixia@zju.edu.cn;卢琴芬女,1972年生,教授,博士生导师,研究方向为直线电机优化设计、驱动控制及应用技术等。E-mail：luqinfen@zju.edu.cn

引用本文:

龚夕霞, 李焱鑫, 卢琴芬. 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[J]. 电工技术学报, 0, (): 109-109. Gong Xixia, Li Yanxin, Lu Qinfen. Thrust Robustness Optimization of Modular Permanent Magnet Linear Synchronous Motor Accounting for Manufacture Tolerance. Transactions of China Electrotechnical Society, 0, (): 109-109.

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[1] 蒋钱, 卢琴芬, 李焱鑫. 双三相永磁直线同步电机的推力波动及抑制[J]. 电工技术学报, 2021, 36(05): 883-892.
Jiang Qian, Lu Qinfen, Li Yanxin.Thrust ripple and depression method of dual three-phase permanent magnet linear synchronous motors[J]. Transactions of China Electrotechnical Society, 2021, 36(05): 883-892.
[2] Cui F, Sun Z, Xu W, et al.Comparative analysis of bilateral permanent magnet linear synchronous motors with different structures[J]. CES Transactions on Electrical Machines and Systems, 2020, 4(2): 142-150.
[3] 沈燚明, 卢琴芬. 初级励磁型永磁直线电机研究现状与展望[J]. 电工技术学报, 2021, 36(11): 2325-2343.
Shen Yiming, Lu Qinfen.Overview of permanent magnet linear machines with primary excitation[J]. Transactions of China Electrotechnical Society, 2021, 36(11): 2325-2343.
[4] 丁文, 李可, 付海刚. 一种12/10极模块化定子混合励磁开关磁阻电机分析[J]. 电工技术学报, 2022, 37(08): 1948-1958.
Ding Wen, Li Ke, Fu Haigang.Analysis of a 12/10-pole modular-stator hybrid-excited switched reluctance machine[J]. Transactions of China Electrotechnical Society, 2022, 37(08): 1948-1958.
[5] 王宇, 张成糕, 郝雯娟. 永磁电机及其驱动系统容错技术综述[J]. 中国电机工程学报, 2022, 42(01): 351-372.
Wang Yu, Zhang Chenggao, Hao Wenjuan.Overview of fault-tolerant technologies of permanent magnet brushless machine and its control system[J], Proceedings of the CSEE, 2022, 42(01): 351-372.
[6] Lei G, Bramerdorfer G, Liu C, et al.Robust Design Optimization of Electrical Machines: A Comparative Study and Space Reduction Strategy[J]. IEEE Transactions on Energy Conversion, 2021, 36(1): 300-313.
[7] Bramerdorfer G.Effect of the Manufacturing Impact on the Optimal Electric Machine Design and Performance[J]. IEEE Transactions on Energy Conversion, 2020, 35(4): 1935-1943.
[8] Xu G, Jia Z, Zhao W, et al.Multi-objective optimization design of inset-surface permanent magnet machine considering deterministic and robust performances[J]. Chinese Journal of Electrical Engineering, 2021, 7(3): 73-87.
[9] Yao Z, Zhao J, Song J, et al.Research on selection criterion of design tolerance for air-core permanent magnet synchronous linear motor[J]. IEEE Transactions on Industrial Electronics (1982), 2021, 68(4): 3336-3347.
[10] Lei G, Wang T, Zhu J, et al.Robust multiobjective and multidisciplinary design optimization of electrical drive systems[J]. CES Transactions on Electrical Machines and Systems, 2018, 2(4): 409-416.
[11] Lei G, Liu C, Li Y, et al.Robust Design Optimization of a High-Temperature Superconducting Linear Synchronous Motor Based on Taguchi Method[J]. IEEE Transactions on Applied Superconductivity, 2019, 29(2): 1-6.
[12] Sun K, Tian S.Multiobjective optimization of IPMSM with FSCW applying rotor notch design for torque performance improvement[J]. IEEE Transactions on Magnetics. 2022, 58(5): 1-9.
[13] Skarmoutsos G A, Gyftakis K N, Mueller M.Analytical prediction of the MCSA signatures under dynamic eccentricity in PM machines with concentrated non-overlapping windings[J]. IEEE Transactions on Energy Conversion. 2022, 37(2): 1011-1019.
[14] 丁强, 王晓琳, 邓智泉等. 大气隙磁通切换无轴承永磁电机径向力绕组设计与比较[J]. 电工技术学报, 2018, 33(11): 2403-2413.
Ding Qiang, Wang Xiaolin, Deng Zhiquan, et al.Design and comparison of radial force winding configurations for wide air-gap flux-switching bearingless permanent-magnet motor[J]. Transactions of China Electrotechnical Society, 2018, 33(11): 2403-2413.
[15] 隋义, 尹佐生, 郑萍等. 单双层混合绕组型低互感五相容错永磁电机的电磁问题研究. 中国电机工程学报[J], 2022, 42(01): 329-340.
Sui Yi, Yin Zuosheng, Zheng Ping, et al.Research on electromagnetic problems of low-mutual-inductance five-phase fault-tolerant PMSM with hybrid single/double-layer FSCW[J]. Proceedings of the CSEE[J], 2022, 42(01): 329-340.
[16] Mehta S, Kabir M A, Pramod P, et al.Segmented Rotor Mutually Coupled Switched Reluctance Machine for Low Torque Ripple Applications. IEEE Transactions on Industry Applications[J]. 2021, 57(4): 3582-3594.
[17] Deb K, Pratap A, Agarwal S, et al.A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197.
[18] Ma Y, Xiao Y, Wang J, et al.Multicriteria optimal latin hypercube design-based surrogate-assisted design optimization for a permanent-magnet vernier machine[J]. IEEE Transactions on Magnetics, 2022, 58(2): 1-5.
[19] Koch P N, Yang R J, Gu L.Design for six sigma through robust optimization[J]. Structural and Multidisciplinary Optimization, 2004, 26(3-4): 235-248.
[20] 唐晓芬. 六西格玛核心教程[M]. 北京: 中国标准出版社, 2006.
Tang Xiaofen.Six sigma core tutorial[M]. Beijing: China Standards Press, 2006.
[21] 谢冰川, 张岳, 徐振耀等. 基于代理模型的电机多学科优化关键技术综述[J]. 电工技术学报, 2022, 37(20): 5117-5143.
Xie Bingchuan, Zhang Yue, Xu Zhenyao, et al.Review on multidisciplinary optimization key technology of electrical machine based on surrogate models[J]. Transactions of China Electrotechnical Society, 2022, 37(20): 5117-5143.
[22] Wu J, Sun X, Zhu J.Accurate torque modeling with PSO-based recursive robust LSSVR for a segmented-rotor switched reluctance motor[J]. CES Transactions on Electrical Machines and Systems, 2020, 4(2): 96-104.
[23] 李雄松, 崔鹤松, 胡纯福等. 平板型永磁直线同步电机推力特性的优化设计[J]. 电工技术学报, 2021, 36(05): 916-923.
Li Xiongsong, Cui Hesong, Cui Hesong, et al.Optimal Design of Thrust Characteristics of Flat-Type Permanent Magnet Linear Synchronous Motor[J]. Transactions of China Electrotechnical Society, 2021, 36(05): 916-923.
[24] 何海婷, 柳亦兵, 巴黎明等. 基于BP神经网络的飞轮储能系统主动磁轴承非线性动力学模型[J]. 中国电机工程学报, 2022, 42(03): 1184-1198.
He Haiting, Liu Yibing, Ba Liming, et al.Nonlinear dynamic model of active magnetic bearing in flywheel system based on BP neural network[J]. Proceedings of the CSEE, 2022, 42(03): 1184-1198.