基于FWA-DBN的航空发电机偏心故障诊断

doi:10.12305/j.issn.1001-506X.2022.05.39

Abstract

Abstract:

A fault diagnosis method based on deep belief network (DBN) optimized by the fireworks algorithm (FWA) is proposed for the problem of small differences in the characteristics of three-phase voltage and current under eccentricity faults in aviation generators with multi-parallel branch winding structures, which are not easy to classify. Firstly, a finite element model of the aviation generator is built and different static and dynamic eccentricity fault data are obtained by simulation. Then the DBN network combining with extreme learning machine (ELM) is optimized by FWA and the optimal DBN-ELM network structure can be obtained. Finally, the fault diagnosis is performed by the ELM classifier. The diagnosis results show that the application of the proposed method for aviation generator eccentricity diagnosis can obtain a higher accuracy with an average accuracy of 99.203% compared with the traditional fault diagnosis methods.

Key words: aviation generator, eccentricity faults, fireworks algorithm (FWA), deep belief network (DBN), extreme learning machine (ELM)

CLC Number:

TM307

Zhangang YANG, Haiyi XU, Boyuan CHENG, Xudong SHI. Aviation generator eccentricity fault diagnosis based on FWA-DBN[J]. Systems Engineering and Electronics, 2022, 44(5): 1757-1764.

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结构	参数
额定功率S/kVA	65
功率因数λ	0.95
额定电压U/V	407
频率f/Hz	400
定子槽数	54
转子极对数p	3
定子外径d_o/mm	225
定子内径d_i/mm	160
最小气隙d_r/mm	1
并联支路数a	2

故障类别	e_A/V	e_B/V	e_C/V
正常	332.46	332.44	332.44
ρ_S=0.25	333.17	332.84	333.27
ρ_S=0.50	332.95	332.97	333.02
ρ_S=0.75	333.11	333.46	333.09
ρ_D=0.25	332.32	332.73	332.63
ρ_D=0.50	332.73	332.89	332.38
ρ_D=0.75	332.81	333.41	332.46

故障类别	U_A/V	U_B/V	U_C/V	i_A/A	i_B/A	i_C/A
正常	330.881	330.032	330.692	120.320	120.012	120.252
ρ_S=0.25	330.784	330.070	330.577	120.285	120.025	120.210
ρ_S=0.50	331.445	330.845	331.108	120.525	120.307	120.403
ρ_S=0.75	332.356	331.784	332.467	120.857	120.649	120.897
ρ_D=0.25	330.708	329.918	330.124	120.257	119.970	120.045
ρ_D=0.50	333.391	333.164	333.442	121.233	121.151	121.252
ρ_D=0.75	332.364	331.583	331.621	120.860	120.576	120.589

参数	取值
迭代次数	200
烟花数量n	50
爆炸半径控制因子α	4
爆炸数量控制因子β	5
爆炸上界x_UB	500
爆炸下界x_LB	1

序号	RBM网络结构	测试集/%	训练集/%	训练时长/s
1	1000-500-250-100	95.233	98.661	46.56
2	800-400-200-100	94.281	96.852	37.44
3	500-250-100-50	83.995	86.997	23.52
4	本文	98.856	99.203	8.16

Aviation generator eccentricity fault diagnosis based on FWA-DBN

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方法	准确率/%	训练时长/s
① FFT+FWA-DBN	87.323	7.66
② SDAE+SVM	92.677	20.81
③ LSTM+Softmax	94.128	22.17

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