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

doi:10.19595/j.cnki.1000-6753.tces.211042

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (9547 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Stator winding inter-turn short-circuit fault is a frequent failure on doubly salient electro-magnetic machine (DSEM). It is difficult to identify the fault accurately by the single fault characteristic because of the less influence by fault windings on the electromagnetic field. Failure to detect a fault condition at an early stage of fault will pose a huge threat to the entire motor system. This paper proposes a comprehensive short-circuit fault diagnosis method for DSEM based on mechanical and electrical signal fusion. The short-circuit fault can be identified effectively by analyzing the structure of the machine and the extraction mechanism of its fault features.
Firstly, the mathematical model of distributed excitation DSEM was established to analyze the changes of current and vibration signals in healthy and fault states, and the extraction mechanism of current fundamental frequency amplitude and vibration signals was studied and adopted for motor fault diagnosis. Secondly, when the inter-turn short-circuit fault occurs, according to the characteristics of current and vibration signals, the current signals are input into SVM and the vibration signals are input into the improved CNN model to obtain fault probability under. Finally, the classification fault diagnosis results of multi-source signals are obtained by using the evidence combination rule of D-S evidence theory. In this integrated model, the problem of low accuracy of single-source diagnosis method is solved, and the model has good reliability, complementarity and high-accuracy.
The results of the experiments conducted on the DSEM platform show that the fault diagnosis experiments using SVM alone are relatively limited in practice, making it difficult to distinguish between faults with small imbalance coefficients and normal conditions. Correspondingly, the SVM is very sensitive to diagnose faults with larger imbalance coefficients and has a high diagnostic accuracy. The vibration signal detected by CNN can make up for the lack of single-source diagnosis. CNN has a relatively impressive accuracy in diagnosing all three operational states. For example, when the fault F1 occurred, the single-source diagnosis method using SVM alone was misjudged, with a probability of 0.668 for normal and 0.33 for fault F1. The CNN method using the vibration signal as input has a probability of 0.227 for normal and 0.736 for fault F1. And the correct diagnostic result is obtained by fusing the two methods using D-S evidence theory in case of inconsistent results. When the diagnosis results of both single-source methods are correct, the fusion can output the correct label with higher probability. From the 100 test sets, the accuracy of the proposed method reaches 95%, which is more reliable, more credible and more robust than the fault diagnosis methods based on single signal.
The following conclusions can be drawn from the experimental analysis:① When the inter-turn short-circuit fault occurs in the DSEM, the fundamental frequency amplitude of the four-phase current will no longer be symmetrical and the difference will be larger. The electromagnetic torque will generate torque harmonics of the 4kth harmonic, which will cause the vibration signal of the fault to change. ② The CNN network in the multi-source signal fusion diagnosis method is improved to find the optimal dropout parameters, which can ensure the success rate of neural network training and prevent the network from overfitting. ③ The proposed multi-source signal fusion fault diagnosis method can complement the advantages of two single-signal fault diagnosis methods, and greatly improve the fault diagnosis accuracy with good robustness and accuracy.

Key words： Doubly salient electro-magnetic machine short-circuit fault mechanical and electrical signal fusion convolution neural network support vector machine

Received: 12 July 2021

PACS:

TM352

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Zhao Yao
	Lu Jiayu
	Li Dongdong
	Yang Fan
	Zhu Miao

Cite this article:

Zhao Yao,Lu Jiayu,Li Dongdong等. A Fault Diagnosis Strategy for Winding Inter-Turn Short-Circuit Fault in Doubly Salient Electro-Magnetic Machine Based on Mechanical and Electrical Signal Fusion[J]. Transactions of China Electrotechnical Society, 2023, 38(1): 204-219.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.211042 OR https://dgjsxb.ces-transaction.com/EN/Y2023/V38/I1/204

[1] Wang Kaimiao, Zhou Bo, Zhou Xingwei, et al.The output voltage control strategy for DSEG with controlled rectification based on conduction angle estimation[J]. IEEE Transactions on Industrial Electronics, 2020, 67(5): 3350-3360.
[2] 赵星, 周波, 史立伟. 一种新型低转矩脉动电励磁双凸极无刷直流电机[J]. 中国电机工程学报, 2016, 36(15): 4249-4258.
Zhao Xing, Zhou Bo, Shi Liwei.A novel electro-magnet doubly salient brushless DC motor with minimum torque ripple[J]. Proceedings of the CSEE, 2016, 36(15): 4249-4258.
[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] 陈云云, 朱孝勇, 全力, 等. 基于参数敏感度的双凸极永磁型双定子电机的优化设计和性能分析[J]. 电工技术学报, 2017, 32(8): 160-168.
Chen Yunyun, Zhu Xiaoyong, Quan Li, et al.Parameter sensitivity optimization design and performance analysis of double-salient permanent-magnet double-stator machine[J]. Transactions of China Electrotechnical Society, 2017, 32(8): 160-168.
[5] 孙林楠, 张卓然, 于立, 等. 定子分布励磁双凸极无刷直流发电机电磁特性研究[J]. 中国电机工程学报, 2017, 37(21): 6218-6226.
Sun Linnan, Zhang Zhuoran, Yu Li, et al.Analysis of electromagnetic performance of doubly salient brushless DC generator with distributed field magnetomotive forces[J]. Proceedings of the CSEE, 2017, 37(21): 6218-6226.
[6] 王瑾, 李岩, 贾建国, 等. 反电动势和凸极率对高效永磁同步磁阻电机稳态特性影响分析[J]. 电工技术学报, 2020, 35(22): 4688-4698.
Wang Jin, Li Yan, Jia Jianguo, et al.Analysis of the influence of back-EMF and saliency ratio on steady-state characteristics of a high efficiency permanent magnet synchronous reluctance motor[J]. Transactions of China Electrotechnical Society, 2020, 35(22): 4688-4698.
[7] 卿龙, 王惠民, 葛兴来. 一种高效率开关磁阻电机转矩脉动抑制方法[J]. 电工技术学报, 2020, 35(9): 1912-1920.
Qing Long, Wang Huimin, Ge Xinglai.A high efficiency torque ripple suppression method for switched reluctance motor[J]. Transactions of China Electrotechnical Society, 2020, 35(9): 1912-1920.
[8] 胡胜龙, 左曙光, 刘明田. 开关磁阻电机非线性径向电磁力解析建模[J]. 电工技术学报, 2020, 35(6): 1189-1197.
Hu Shenglong, Zuo Shuguang, Liu Mingtian.Analytical modeling of nonlinear radial electromagnetic force in switched reluctance motors[J]. Transactions of China Electrotechnical Society, 2020, 35(6): 1189-1197.
[9] Du Longxin, Liu Xiping, Fu Jiesheng, et al.Design and optimization of reverse salient permanent magnet synchronous motor based on controllable leakage flux[J]. CES Transactions on Electrical Machines and Systems, 2021, 5(2): 163-173.
[10] Jiang Xuefeng, Wang Shaoshuai, Li Qiang, et al.Design and optimization of dual-winding fault-tolerant permanent magnet motor[J]. CES Transactions on Electrical Machines and Systems, 2019, 3(1): 45-53.
[11] Gan Chun, Chen Yu, Qu Ronghai, et al.An overview of fault-diagnosis and fault-tolerance techniques for switched reluctance machine systems[J]. IEEE Access, 2019, 7: 174822-174838.
[12] Gan Chun, Wu Jianhua, Yang Shiyou, et al.Wavelet packet decomposition-based fault diagnosis scheme for SRM drives with a single current sensor[J]. IEEE Transactions on Energy Conversion, 2016, 31(1): 303-313.
[13] Wang Bo, Wang Jiabin, Griffo A, et al.Stator turn fault detection by second harmonic in instantaneous power for a triple-redundant fault-tolerant PM drive[J]. IEEE Transactions on Industrial Electronics, 2018, 65(9): 7279-7289.
[14] Qi Yuan, Bostanci E, Zafarani M, et al.Severity estimation of interturn short circuit fault for PMSM[J]. IEEE Transactions on Industrial Electronics, 2019, 66(9): 7260-7269.
[15] Lee S T, Hur J.Detection technique for stator inter-turn faults in BLDC motors based on third-harmonic components of line currents[J]. IEEE Transactions on Industry Applications, 2017, 53(1): 143-150.
[16] Fonseca D S B, Marques Cardoso A J. On-line inter-turn short-circuit fault diagnosis in switched reluctance motors[C]//2019 IEEE International Electric Machines & Drives Conference (IEMDC), San Diego, CA, USA, 2019: 198-202.
[17] 夏一文, 张卓然, 张健, 等. 基于反电势电流的电励磁双凸极电机驱动电路单管开路故障诊断研究[J]. 电工技术学报, 2020, 35(23): 4888-4897.
Xia Yiwen, Zhang Zhuoran, Zhang Jian, et al.Research on single power switch open circuit fault diagnosis of doubly salient eletromagnetic motor drive circuit based on the back electromotive force current[J]. Transactions of China Electrotechnical Society, 2020, 35(23): 4888-4897.
[18] 胡朝燕, 周波, 魏佳丹. 双凸极电机全桥变换器单管开路故障在线诊断[J]. 中国电机工程学报, 2009, 29(33): 111-116.
Hu Chaoyan, Zhou Bo, Wei Jiadan.On-line diagnosis of single transistor open-circuit fault in full-bridge converter of doubly salient electro-magnet motor[J]. Proceedings of the CSEE, 2009, 29(33): 111-116.
[19] 崔江, 郭瑞东, 张卓然, 等. 基于改进DBN的发电机旋转整流器故障特征提取技术[J]. 中国电机工程学报, 2020, 40(7): 2369-2376, 2415.
Cui Jiang, Guo Ruidong, Zhang Zhuoran, et al.Generator rotating rectifier fault feature extraction technique based on improved DBN[J]. Proceedings of the CSEE, 2020, 40(7): 2369-2376, 2415.
[20] 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): 3797-3806.
[21] Seshadrinath J, Singh B, Panigrahi B K.Vibration analysis based interturn fault diagnosis in induction machines[J]. IEEE Transactions on Industrial Informatics, 2014, 10(1): 340-350.
[22] Huang Yang, Chen C H, Huang C J.Motor fault detection and feature extraction using RNN-based variational autoencoder[J]. IEEE Access, 2019, 7: 139086-139096.
[23] Hoang D T, Kang H J.A motor current signal-based bearing fault diagnosis using deep learning and information fusion[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(6): 3325-3333.
[24] 施耀华, 冯延晖, 任铭, 等. 融合电流和振动信号的永磁同步风电系统变流器故障诊断方法研究[J]. 中国电机工程学报, 2020, 40(23): 7750-7760.
Shi Yaohua, Feng Yanhui, Ren Ming, et al.Study on fault diagnosis method of converter in permanent magnet synchronous wind power system by fusion of current and vibration signals[J]. Proceedings of the CSEE, 2020, 40(23): 7750-7760.
[25] Liao Y, Liang F, Lipo T A.A novel permanent magnet motor with doubly salient structure[C]//Conference Record of the 1992 IEEE Industry Applications Society Annual Meeting, Houston, TX, USA, 2002: 308-314.
[26] Zhu Z Q, Lee B, Huang Liren, et al.Contribution of current harmonics to average torque and torque ripple in switched reluctance machines[J]. IEEE Transactions on Magnetics, 2017, 53(3): 1-9.
[27] 王栋悦, 谷怀广, 魏书荣, 等. 基于机电信号融合的DFIG定子绕组匝间短路故障诊断[J]. 电力系统自动化, 2020, 44(9): 171-178.
Wang Dongyue, Gu Huaiguang, Wei Shurong, et al.Diagnosis of inter-turn short-circuit fault in stator windings of DFIG based on mechanical and electrical signal fusion[J]. Automation of Electric Power Systems, 2020, 44(9): 171-178.
[28] Shao Siyu, Yan Ruqiang, Lu Yadong, et al.DCNN-based multi-signal induction motor fault diagnosis[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(6): 2658-2669.
[29] 李永刚, 王罗, 李俊卿, 等. 基于多源信息融合的同步发电机转子绕组匝间短路故障识别[J]. 电力系统自动化, 2019, 43(16): 162-167, 191.
Li Yonggang, Wang Luo, Li Junqing, et al.Identification of inter-turn short-circuit fault in rotor windings of synchronous generator based on multi-source information fusion[J]. Automation of Electric Power Systems, 2019, 43(16): 162-167, 191.