一种基于GAMF-CNN的航空发电机整流器故障诊断技术

doi:10.7527/S1000-6893.2024.30398

Abstract

Abstract:

To address the problems such as less actual sample data of aerospace generator rectifier faults， a fault diagnosis technique based on Gramian Angular Multiply Field-Convolutional Neural Network （GAMF-CNN） is presented. First， original rectifier fault signals are collected and preprocessed， and the one-dimensional time series signals are transformed into GAMF images， so that the fault diagnosis problem can be transformed into an image recognition problem. Second， with the help of deep transfer learning concept， a convolutional neural network is used to transfer the fault feature knowledge obtained from the simulation to the real generator rectifier that lacks fault data. Finally， the aerospace generator rectifier fault diagnosis problem with small sample data is solved. Experimental verification and comparison with some existing methods find that the propsoed method can realize diagnosis and localization of faulty diodes with high accuracy.

Key words: aerospace generator, rectifier, fault diagnosis, Gramian angular multiply field, transfer learning

CLC Number:

V240.2

Jiang CUI, Fan ZHOU, Yongfan CHEN, Li YU, Zhuoran ZHANG. A technique for aerospace generator rectifier fault diagnosis based on GAMF-CNN[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(24): 330398.

Figures/Tables 15

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References 25

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故障模式	故障描述	故障二极管
模式 Ⅰ	健康	无
模式 Ⅱ	单管开路	D1，D2，D3
模式 Ⅱ	单管开路	D4，D5，D6
模式 Ⅲ	双管开路	D1D2，D3D4，D5D6
		D1D3，D1D5，D3D5
		D2D4，D2D6，D4D6
		D1D4，D1D6，D2D3
		D2D5，D3D6，D4D5

名称	数值	名称	数值
定子极数	12	转子内径/mm	35
转子极数	8	额定转速/（r·min^-1）	3 000
铁芯长度/mm	68	额定输出电压/VDC	54
气隙长度/mm	0.25	额定输出功率/kW	2

三相电流	A相	B相	C相
波形正负均有	A±	B±	C±
仅有正半波形	A+	B+	C+
仅有负半波形	A－	B－	C－
波形正负均无	A/	B/	C/

方法	诊断准确率/%
时域图像-CNN	65.64
本文方法	90.16
GADF-CNN	75.45
GASF-CNN	74.53

类型	数据集	转速/（r·min^-1）	励磁源/V	负载/Ω
仿真数据	i	1 000	17.94	22
	ii	1 500	17.94	22
	iii	2 000	17.94	22
	iv	3 000	17.94	22
	v	6 000	17.94	22
实际数据	vi	1 000	1.6， 4.8， 8.0， 9.8	2
	vii	1 500	1.6， 4.8， 8.0， 9.8	2
	viii	2 000	1.6， 4.8， 8.0， 9.8	2
	ix	1 000	1.6， 4.8， 8.0， 9.8	2
		1 500
		2 000

A technique for aerospace generator rectifier fault diagnosis based on GAMF-CNN

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迁移任务	准确率/%	测试时间/s	迁移任务	准确率/%	测试时间/s
i→vi	93.94	0.71	iii→viii	89.39	0.68
i→vii	84.85	0.61	iii→ix	87.37	0.63
i→viii	90.91	0.63	iv→vi	95.45	0.62
i→ix	92.42	0.60	iv→vii	84.24	0.66
ii→vi	92.91	0.77	iv→viii	89.39	0.70
ii→vii	89.90	0.69	iv→ix	87.37	0.72
ii→viii	90.91	0.66	v→vi	88.34	0.70
ii→ix	87.88	0.60	v→vii	91.36	0.75
iii→vi	95.45	0.71	v→viii	91.36	0.75
iii→vii	88.79	0.83	v→ix	88.38	0.60

迁移任务	准确率/%
迁移任务	本文方法	TCA	JDA	BDA	MEDA
平均	90.16	75.93	73.99	74.63	76.04
i→vi	93.94	72.90	66.76	66.53	77.56
i→vii	84.85	79.26	76.36	75.51	88.07
i→viii	90.91	81.02	75.97	77.44	89.66
i→ix	92.42	77.50	71.82	71.82	87.50
ii→vi	92.91	70.85	66.19	67.39	69.66
ii→vii	89.90	78.41	74.49	73.81	85.85
ii→viii	90.91	78.75	75.68	76.25	90.00
ii→ix	87.88	76.70	72.33	72.73	82.90
iii→vi	95.45	72.33	66.93	69.89	76.02
iii→vii	88.79	79.32	74.94	79.32	77.67
iii→viii	89.39	81.70	78.52	78.18	88.01
iii→ix	87.37	77.78	74.20	76.88	74.94
iv→vi	95.45	63.35	69.03	68.58	68.07
iv→vii	84.24	72.67	75.91	78.35	59.94
iv→viii	89.39	74.43	78.69	79.94	63.01
iv→ix	87.37	73.64	74.43	75.23	59.83
v→vi	88.34	70.63	72.39	69.60	66.40
v→vii	93.94	78.18	77.39	77.33	70.73
v→viii	91.36	81.88	81.19	81.31	82.89
v→ix	88.38	77.27	76.48	76.48	61.99

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