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

doi:10.11985/2023.04.013

Abstract

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

CLC Number:

TM341

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.

Figures/Tables 13

References 24

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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