采用贝叶斯优化和多尺度卷积网络的五相永磁同步电机匝间短路诊断*

doi:10.11985/2023.04.013

电气工程学报 ›› 2023, Vol. 18 ›› Issue (4): 114-123.doi: 10.11985/2023.04.013

• 特邀专栏:轻量化高可靠永磁电机系统 • 上一篇下一篇

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采用贝叶斯优化和多尺度卷积网络的五相永磁同步电机匝间短路诊断^*

戴显阳(), 陈前(), 宋向金(), 刘正蒙(), 徐高红()

江苏大学电气信息工程学院镇江 212013

收稿日期:2023-07-19 修回日期:2023-08-28 出版日期:2023-12-25 发布日期:2024-01-12
通讯作者: 陈前，男，1986年生，博士，教授。主要研究方向为高可靠永磁电机系统。E-mail：chenqian0501@ujs.edu.cn
作者简介:戴显阳，男，1997年生，硕士研究生。主要研究方向为永磁电机故障诊断。E-mail：2222107117@stmail.ujs.edu.cn
宋向金，女，1989年生，博士，讲师。主要研究方向为电机状态监测与故障诊断。E-mail：songxiangjin@ujs.edu.cn
刘正蒙，男，1991 年生，博士，讲师。主要研究方向为永磁电机设计。E-mail：lzm@ujs.edu.cn
徐高红，女，1987年生，博士，讲师。主要研究方向为永磁电机设计、分析与应用。E-mail：xgh@ujs.edu.cn
基金资助:
*国家自然科学基金资助项目(52077097)

ITSC Fault Diagnosis for Five-phase Permanent Magnet Synchronous Motors Using Bayesian Optimization and Multiscale Convolutional Neural Network

DAI Xianyang(), CHEN Qian(), SONG Xiangjin(), LIU Zhengmeng(), XU Gaohong()

School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013

Received:2023-07-19 Revised:2023-08-28 Online:2023-12-25 Published:2024-01-12

摘要/Abstract

摘要：

传统神经网络算法虽成功应用于永磁电机的匝间短路故障诊断中，但其在噪声干扰下难以提取出足够的故障特征量，且依靠经验选取的模型超参数无法达到性能最优，限制了其应用范围。为此，提出了一种采用贝叶斯优化的多尺度卷积神经网络算法，并将其用于五相永磁同步电机早期ITSC故障诊断。首先，使用多尺度卷积神经网络采集足够的故障特征。其次，引入贝叶斯优化算法实现模型超参数全局寻优，节省调参时间。最后，该算法通过提取实际电流信号数据实现不同短路情况下的早期ITSC故障诊断。为了证明所提算法的优越性，将其与其他四种算法进行比较，结果表明所提方法的准确率更高。

关键词: 故障诊断, 匝间短路, 五相永磁同步电机, 多尺度卷积网络, 贝叶斯优化

Abstract:

Although the traditional neural network algorithm has been successfully applied to the inter-turn short circuit(ITSC) fault diagnosis of permanent magnet motors, it is difficult to extract enough fault features under the noise interference, and the model hyperparameters selected by relying on experience cannot reach the optimal performance, which restricts its wide application. Thus, a multiscale convolutional neural network algorithm using Bayesian optimization(BO) is proposed for early ITSC fault diagnosis of five-phase permanent magnet synchronous motors(FPPMSM). Firstly, sufficient fault features are collected using a multiscale convolutional neural network(MSCNN). Secondly, the Bayesian optimization algorithm is introduced to achieve global optimization of model hyperparameters and to save tuning time. Finally, the algorithm achieves early ITSC fault diagnosis in different short-circuit situations by extracting the actual current signal data. To prove the effectiveness of the proposed algorithm, it is compared with other four algorithms and the experimental results show that the proposed method is more accurate.

Key words: Fault diagnosis, inter-turn short circuit faults, five-phase permanent magnet synchronous motors, multiscale convolutional neural network, Bayesian optimization

中图分类号:

TM341

戴显阳, 陈前, 宋向金, 刘正蒙, 徐高红. 采用贝叶斯优化和多尺度卷积网络的五相永磁同步电机匝间短路诊断^*[J]. 电气工程学报, 2023, 18(4): 114-123.

DAI Xianyang, CHEN Qian, SONG Xiangjin, LIU Zhengmeng, XU Gaohong. ITSC Fault Diagnosis for Five-phase Permanent Magnet Synchronous Motors Using Bayesian Optimization and Multiscale Convolutional Neural Network[J]. Journal of Electrical Engineering, 2023, 18(4): 114-123.

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参考文献 24

[1]	DENG Wenzhe, ZUO Shuguang. Comparative study of sideband electromagnetic force in internal and external rotor PMSMs with SVPWM technique[J]. IEEE Transactions on Industrial Electronics, 2019, 66(2):956-966. doi: 10.1109/TIE.41
[2]	GU B G. Study of IPMSM interturn faults part I:Development and analysis of models with series and parallel winding connections[J]. IEEE Transactions on Power Electronics, 2016, 31(2):5931-5943. doi: 10.1109/TPEL.2015.2496142
[3]	JEONG H, MOON S, KIM S W. An early stage interturn fault diagnosis of PMSMs by using negative-sequence components[J]. IEEE Transactions on Industrial Electronics, 2017, 64(7):5701-5708. doi: 10.1109/TIE.2017.2677355
[4]	WOLKIEWICZ M, TARCHALA G, ORLOWSKA- KOWALSKA T, et al. Online stator interturn short circuits monitoring in the DFOC induction-motor drive[J]. IEEE Transactions on Industrial Electronics, 2016, 63(4):2517-2528. doi: 10.1109/TIE.2016.2520902
[5]	HANG Jun, ZHANG Jibo, XIA Mengjie, et al. Interturn fault diagnosis for model-predictive-controlled-PMSM based on cost function and wavelet transform[J]. IEEE Transactions on Power Electronics, 2020, 35(6):6405-6418. doi: 10.1109/TPEL.63
[6]	陈勇, 梁洪, 王成栋, 等. 基于改进小波包变换和信号融合的永磁同步电机匝间短路故障检测[J]. 电工技术学报, 2020, 35(S1):228-234.
	CHEN Yong, LIANG Hong, WANG Chengdong, et al. Detection of interturn short-circuit faults in permanent magnet synchronous motors based on improved wavelet packet transform and signal fusion[J]. Transactions of China Electrotechnical Society, 2020, 35(S1):228-234.
[7]	张业成, 刘国海, 陈前. 基于电流波动特征的永磁同步电机匝间短路与局部退磁故障分类诊断研究[J]. 电工技术学报, 2022, 37(7):1634-1643,1653.
	ZHANG Yecheng, LIU Guohai, CHEN Qian. Classification and diagnosis of interturn short-circuit and local demagnetization faults in permanent magnet synchronous motors based on current fluctuation characteristics[J]. Transactions of China Electrotechnical Society, 2022, 37(7):1634-1643,1653.
[8]	魏东, 刘侃, 丁荣军, 等. 基于多重同步压缩变换的永磁同步电机初期匝间短路故障检测[J]. 电工技术学报, 2022, 37(18):4651-4663.
	WEI Dong, LIU Kan, DING Rongjun, et al. Interturn short-circuit fault detection in permanent magnet synchronous motors based on multiple synchronous compression transformation[J]. Transactions of China Electrotechnical Society, 2022, 37(18):4651-4663.
[9]	丁石川, 王清明, 杭俊, 等. 计及模型预测控制的永磁同步电机匝间短路故障诊断[J]. 中国电机工程学报, 2019, 39(12):3697-3707.
	DING Shichuan, WANG Qingming, HANG Jun, et al. Inter-turn fault diagnosis of permanent magnet synchronous machine considering model predictive control[J]. Proceedings of the CSEE, 2019, 39(12):3697-3707.
[10]	魏书荣, 任子旭, 符杨, 等. 基于双侧磁链观测差的海上双馈风力发电机转子绕组匝间短路早期故障辨识[J]. 中国电机工程学报, 2019, 39(5):1470-1479.
	WEI Shurong, REN Zixu, FU Yang, et al. Early fault identification of interturn short-circuit in rotor windings of offshore doubly-fed wind turbines based on the observed difference in magnetic chains on both sides[J]. Proceedings of the CSEE, 2019, 39(5):1470-1479.
[11]	黄建, 尹佐生, 王天乙, 等. 基于容错齿磁通的五相永磁电机匝间短路故障诊断[J]. 电工技术学报, 2023, 38(14):3733-3744.
	HUANG Jian, YI Zuosheng, WANG Tianyi, et al. Five-phase permanent magnet motor interturn short-circuit fault diagnosis based on fault-tolerant tooth flux[J]. Transactions of China Electrotechnical Society, 2023, 38(14):3733-3744.
[12]	杭俊, 胡齐涛, 丁石川, 等. 基于电流残差矢量模平方的永磁同步电机匝间短路故障鲁棒检测与定位方法研究[J]. 中国电机工程学报, 2022, 42(1):340-351.
	HANG Jun, HU Qitao, DING Shichuan, et al. Robust detection and localization of interturn short-circuit faults in permanent magnet synchronous motors based on current residual vector mode squared[J]. Proceedings of the CSEE, 2022, 42(1):340-351.
[13]	GE Yuxue, SONG Bifeng, PEI Yang, et al. Analytical expressions of isolation indicators for permanent-magnet synchronous machines under stator short-circuit faults[J]. IEEE Transactions on Energy Conversion, 2019, 34(2):984-992. doi: 10.1109/TEC.60
[14]	AUBERT B, REGNIER J, CAUX S, et al. Kalman-filter-based indicator for online interturn short circuits detection in permanent-magnetsynchronous generators[J]. IEEE Transactions on Industrial Electronics, 2015, 62(3):1921-1930. doi: 10.1109/TIE.2014.2348934
[15]	WEI Dong, LIU Kan, HU Wei, et al. Short-time adaline based fault feature extraction for inter-turn short circuit diagnosis of PMSM via residual insulation monitoring[J]. IEEE Transactions on Industrial Electronics, 2023, 70(3):3103-3114. doi: 10.1109/TIE.2022.3167164
[16]	史丽萍, 汤家升, 王攀攀, 等. 采用最优小波树和改进BP神经网络的感应电动机定子故障诊断[J]. 电工技术学报, 2015, 30(24):38-45.
	SHI Liping, TANG Jiasheng, WANG Panpan, et al. Stator fault diagnosis of induction motors using optimal wavelet trees and improved BP neural networks[J]. Transactions of China Electrotechnical Society, 2015, 30(24):38-45.
[17]	ALI M Z, SHABBIR M N S K, LIANG Xiaodong, et al. Machine learning-based fault diagnosis for single- and multi-faults in induction motors using measured stator currents and vibration signals[J]. IEEE Transactions on Industry Applications, 2019, 55(3):2378-2391. doi: 10.1109/TIA.28
[18]	MARAABA L S, MILHEM A S, NEMER I A, et al. Convolutional neural network-based inter-turn fault diagnosis in LSPMSMs[J]. IEEE Access, 2020, 8:81960-81970. doi: 10.1109/Access.6287639
[19]	张玉良, 马宏忠, 朱昊, 等. 基于稀疏深度森林的调相机轻微定子匝间短路故障诊断[J]. 高电压技术, 2022, 48(5):1875-1883.
	ZHANG Yuliang, MA Hongzhong, ZHU Hao, et al. Sparse depth forest-based fault diagnosis of minor stator inter-turn short circuit in regulating machines[J]. High Voltage Engineering, 2022, 48(5):1875-1883.
[20]	LEE H, JEONG H, KOO G, et al. Attention recurrent neural network-based severity estimation method for interturn short-circuit fault in permanent magnet synchronous machines[J]. IEEE Transactions on Industrial Electronics, 2021, 68(4):3445-3453. doi: 10.1109/TIE.41
[21]	赵耀, 陆佳煜, 李东东, 等. 基于机电信号融合的电励磁双凸极电机绕组匝间短路故障诊断[J]. 电工技术学报, 2023, 38(1):204-219.
	ZHAO Yao, LU Jiayu, LI Dongdong, et al. Electro-mechanical signal fusion based fault diagnosis of electrically excited biconvex motor winding inter-turn short circuit[J]. Transactions of China Electrotechnical Society, 2023, 38(1):204-219.
[22]	LIU Ruonan, WANG Fei, YANG Boyuan, et al. Multiscale kernel based residual convolutional neural network for motor fault diagnosis under nonstationary conditions[J]. IEEE Transactions on Industrial Informatics, 2020, 16(6):204-219.
[23]	CUI Ronghua, FAN Ying, LI Chenxue. On-line inter-turn short-circuit fault diagnosis and torque ripple minimization control strategy based on OW five-phase BFTHE-IPM[J]. IEEE Transactions on Energy Conversion, 2018, 33(4):2200-2209. doi: 10.1109/TEC.2018.2851615
[24]	崔佳旭, 杨博. 贝叶斯优化方法和应用综述[J]. 软件学报, 2018, 29(10):3068-3090.
	CUI Jiaxu, YANG Bo. A review of Bayesian optimization methods and applications[J]. Journal of Software, 2018, 29(10):3068-3090.

标签	转速/ (r/min)	负载/ (N·m)	匝数比(%)	训练集	测试集
HLA	300	1	0	960	240
HLB	500	1	0	960	240
A1R0.1	300	1	1.4	960	240
A2R0.1	300	1	7.1	960	240
A3R0.1	300	1	14.2	960	240
B1R0.1	500	1	1.4	960	240
B2R0.1	500	1	7.1	960	240
B3R0.1	500	1	14.2	960	240

序号	参数	描述	取值
1	—	训练集	0.8×总样本
2	—	测试集	0.2×总样本
3	a	卷积尺寸1	[1,10]
4	b	卷积尺寸2	[10,30]
5	c	卷积尺寸3	[30,50]
6	d	卷积尺寸4	[50,100]
7	e	卷积尺寸5	[100,150]
8	f	卷积核数量	[16,64]
8	D	优化器	Adam、SGD
10	—	批次	32
11	—	遗弃率	0.25
12	—	学习率	动态学习率
13	—	全连接层	节点数100
14	—	输出层	分类数8

序号	参数	描述	取值
1	—	训练集	0.8×总样本
2	—	测试集	0.2×总样本
3	a	卷积尺寸1	10×1
4	b	卷积尺寸2	20×1
5	c	卷积尺寸3	35×1
6	d	卷积尺寸4	70×1
7	e	卷积尺寸5	130×1
8	f	卷积核数量	64
9	D	优化器	Adam
10	—	批次	32
11	—	遗弃率	0.25
12	—	学习率	动态学习率
13	—	全连接层	节点数100
14	—	输出层	分类数8

采用贝叶斯优化和多尺度卷积网络的五相永磁同步电机匝间短路诊断^*

ITSC Fault Diagnosis for Five-phase Permanent Magnet Synchronous Motors Using Bayesian Optimization and Multiscale Convolutional Neural Network

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