基于半监督深度学习的雷达收发组件故障诊断

doi:10.12305/j.issn.1001-506X.2023.10.38

Abstract

Abstract:

A large number of sensors are deployed in the new generation phased array radar for T/R components, which provides prerequisites for their data-driven fault diagnosis. However, actual detection data lack labels representing specific fault mode. Combining the advantages of deep belief network (DBN) in feature self-learning and the ability of auto-encoder (AE) to reconstruct input data, a fault feature extraction and intelligent diagnosis method based on DBN-AE semi-supervised learning model is proposed, and its structure is optimized by the firework algorithm (FWA). The original unlabeled data is directly used to train the DBN-AE model to extract deep feature, then the relation model between deep features and fault modes is built through supervised retraining. The proposed method is verified to enhance the fault diagnosis accuracy and intelligence of the T/R module on a certain phased array radar.

Key words: phased array radar, T/R module, fault diagnosis, deep belief network (DBN), deep auto-encoder (AE), firework algorithm (FWA)

CLC Number:

TP277

Yukun CHEN, Hui YU, Ningyun LU. Fault diagnosis of radar T/R module based on semi-supervised deep learning[J]. Systems Engineering and Electronics, 2023, 45(10): 3329-3337.

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故障模式	故障表现
电源欠压	电源电压偏低, 发射功放输出功率低
电源过压	电源电压偏高, 发射功放输出功率高
发射通道故障	发射功放输出功率下降或为零
接收通道故障	接收信号功率下降或无接收信号
发射和接收通道故障	发射和接收通道监测幅度均异常

序号	状态量名称	变量描述	单位
1~8	TxMODULE_OUTPOWER1~8	发射功放输出功率	dB
9~16	DAM_CHANNEL_REC1~8	接收通道接收信号	dB
17~24	DAM_CHANNEL_NOISE1~8	接收通道噪声信号	dB
25	RT_PS_V	收发电源电压	V
26	RT_PS_C	收发电源电流	A

参数	数值	参数	数值
学习率	0.1	预训练迭代次数	20
初始权重	随机值	编码部分隐藏层数	4
初始偏置	0	解码部分隐藏层数	4
初始动量	0.5	输入层单元数	26
稳态动量	0.9	输出层单元数	26
权重衰减	0.000 2	反向微调迭代次数	500

参数	数值	参数	数值
初始烟花数	10	迭代次数	50
爆炸半径调节常数	2	变量维数	4
爆炸数目调节常数	6	变量上界	50
变异火花数	5	变量下界	1

方法	训练误差	测试误差	时间/s
本文算法	0.053 8	0.059 2	176.39
PSO-DBN-AE	0.055 6	0.061 6	180.55
未优化DBN-AE	0.059 5	0.066 2	146.08
单一DAE	0.071 2	0.076 3	133.45

Fault diagnosis of radar T/R module based on semi-supervised deep learning

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故障诊断方法		训练准确率	测试准确率
半监督学习方法	本文方法	96.60	96.00
	PSO-DBN-AE	94.67	94.00
	DBN-AE	94.27	93.33
有监督学习方法	单一DBN	93.53	92.67
	DBN-ELM	94.13	93.33
	SVM	92.10	90.33
	朴素贝叶斯	90.60	89.33

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