WPA-IGA-BP神经网络的模拟电路故障诊断

doi:10.12305/j.issn.1001-506X.2021.04.32

Abstract

Abstract:

In the view of the difficulty in feature extraction and failure signal classification in analog circuit with gradual change, an immune genetic algorithm (IGA) is proposed to optimize the parameter optimization process in back propagation (BP) neural network, so as to realize analog circuit fault diagnosis. Firstly, the wavelet package analysis (WPA) is used to decompose and reconstruct the output response of analog circuit in four layers, and the feature vector is extracted. Then, the IGA optimized BP neural network is used for training and testing to realize fault diagnosis of different faults. Finally, the two simulation methods are verified by simulation. The experimental results show that, compared with the BP neural network before optimization, the proposed method improves the accuracy of fault diagnosis by about 15%.

Key words: back propagation (BP) neural network, immune genetic algorithm (IGA), analog circuit, feature extraction, fault diagnosis

CLC Number:

Li WANG, Ziqi LIU. Fault diagnosis of analog circuit for WPA-IGA-BP neural network[J]. Systems Engineering and Electronics, 2021, 43(4): 1133-1143.

Figures/Tables 21

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References 31

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故障编号	故障类型	标称值	故障值
F0	正常	—	—
F1	R1↑	40 kΩ	45 kΩ~90 kΩ
F2	R1↓	40 kΩ	35 kΩ~10 kΩ
F3	R2↑	18 kΩ	20 kΩ~40 kΩ
F4	R3↑	1 kΩ	1.2 kΩ~3 kΩ
F5	R3↓	1 kΩ	0.9 kΩ~0.1 kΩ
F6	R4↑	3 kΩ	3.5 kΩ~6.5 kΩ
F7	C3↑	470 μF	500 μF~750 μF

故障编号	故障类型	标称值	故障值
F0	正常	—	—
F1	R1↑	6.2 kΩ	9 kΩ~12 kΩ
F2	R1↓	6.2 kΩ	3 kΩ~1 kΩ
F3	R3↑	6.2 kΩ	9 kΩ~12 kΩ
F4	C1↓	5 μF	2 μF~0.5 μF
F5	R4↑	1.6 kΩ	2.4 kΩ~4 kΩ
F6	C2↑	5 μF	10 μF~15 μF
F7	R7↑	10 kΩ	15 kΩ~20 kΩ
F8	R7↓	10 kΩ	5 kΩ~1 kΩ

诊断模型	故障模式	训练样本		测试样本
诊断模型	故障模式	样本数量	时间/s	样本数量	正确数	正确率/%	时间/s
BP神经网络	正常	66	31.958 24	33	30	91	0.127 729 3
	R1↑	66	31.958 24	33	21	64	0.127 729 3
	R1↓	66	31.958 24	33	23	70	0.127 729 3
	R3↑	66	31.958 24	33	28	85	0.127 729 3
	C1↓	66	31.958 24	33	29	88	0.127 729 3
	R4↑	66	31.958 24	33	33	100	0.127 729 3
	C2↑	66	31.958 24	33	31	94	0.127 729 3
	R7↑	66	31.958 24	33	20	61	0.127 729 3
	R7↓	66	31.958 24	33	19	58	0.127 729 3
GA-BP神经网络	正常	66	28.196 34	33	32	97	0.134 497
	R1↑	66	28.196 34	33	27	82	0.134 497
	R1↓	66	28.196 34	33	28	85	0.134 497
	R3↑	66	28.196 34	33	33	100	0.134 497
	C1↓	66	28.196 34	33	30	91	0.134 497
	R4↑	66	28.196 34	33	33	100	0.134 497
	C2↑	66	28.196 34	33	31	94	0.134 497
	R7↑	66	28.196 34	33	25	76	0.134 497
	R7↓	66	28.196 34	33	23	70	0.134 497
IGA-BP神经网络	正常	66	24.315 7	33	33	100	0.014 457 4
	R1↑	66	24.315 7	33	30	91	0.014 457 4
	R1↓	66	24.315 7	33	32	97	0.014 457 4
	R3↑	66	24.315 7	33	33	100	0.014 457 4
	C1↓	66	24.315 7	33	33	100	0.014 457 4
	R4↑	66	24.315 7	33	33	100	0.014 457 4
	C2↑	66	24.315 7	33	33	100	0.014 457 4
	R7↑	66	24.315 7	33	30	91	0.014 457 4
	R7↓	66	24.315 7	33	31	94	0.014 457 4

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Fault diagnosis of analog circuit for WPA-IGA-BP neural network

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诊断模型	故障模式	训练样本		测试样本
诊断模型	故障模式	样本数量	时间/s	样本数量	正确数	正确率/%	时间/s
BP神经网络	正常	60	30.656 45	25	25	100	0.104 878 1
	R1↑	60	30.656 45	25	23	92	0.104 878 1
	R1↓	60	30.656 45	25	24	96	0.104 878 1
	R2↑	60	30.656 45	25	23	92	0.104 878 1
	R3↑	60	30.656 45	25	20	80	0.104 878 1
	R3↓	60	30.656 45	25	22	88	0.104 878 1
	R4↑	60	30.656 45	25	24	96	0.104 878 1
	C3↑	60	30.656 45	25	13	52	0.104 878 1
GA-BP神经网络	正常	60	27.678 97	25	25	100	0.150 623
	R1↑	60	27.678 97	25	21	84	0.150 623
	R1↓	60	27.678 97	25	24	96	0.150 623
	R2↑	60	27.678 97	25	22	88	0.150 623
	R3↑	60	27.678 97	25	23	92	0.150 623
	R3↓	60	27.678 97	25	25	100	0.150 623
	R4↑	60	27.678 97	25	23	92	0.150 623
	C3↑	60	27.678 97	25	19	76	0.150 623
IGA-BP神经网络	正常	60	22.432 45	25	25	100	0.016 607 1
	R1↑	60	22.432 45	25	25	100	0.016 607 1
	R1↓	60	22.432 45	25	25	100	0.016 607 1
	R2↑	60	22.432 45	25	25	100	0.016 607 1
	R3↑	60	22.432 45	25	25	100	0.016 607 1
	R3↓	60	22.432 45	25	25	100	0.016 607 1
	R4↑	60	22.432 45	25	25	100	0.016 607 1
	C3↑	60	22.432 45	25	23	92	0.016 607 1