基于红外温度序列的电路板故障诊断研究  

Research on circuit board fault diagnosis based on infrared temperature series

作　　者：郝建新 王力[2] Hao Jianxin;Wang Li(Engineering Techniques Training Center,Civil Aviation University of China,Tianjin 300300,China;Vocational and Technical College,Civil Aviation University of China,Tianjin 300300,China)

机构地区：[1]中国民航大学工程技术训练中心,天津300300 [2]中国民航大学职业技术学院,天津300300

出　　处：《红外与激光工程》2023年第4期51-62,共12页Infrared and Laser Engineering

基　　金：国家自然科学基金(U173319)。

摘　　要：电路板红外温度序列包含了丰富的故障类别信息,充分利用其局部与全局特征可以提高电路板故障诊断的准确率。为此,文中提出了一种由特征提取网络(Features Extraction Network,FEN)与关系学习网络(Relationship Learning Network,RLN)并行构成的可综合利用温度序列局部特征及特征间关系的电路板故障诊断模型。其中,FEN基于多尺度膨胀卷积(Multi-scale Dilated CNN,MDCNN)残差结构搭建,可在不增加训练参数的前提下构建多层次感受野,学习温度序列不同范围的空间特征;RLN基于嵌入长短期记忆网络的注意力机制(Long Short-Term Memory hybridized with Attention,LSTMwAtt)结构搭建,通过控制温度序列信息传递来学习特征重要性并分配权重,挖掘不同位置特征间的相关性。实验结果显示,所提模型在两个自建电路板温度序列测试数据集上的诊断性能优于同类型的FCN、MFCN、LSTM和LSTM-FCN,故障诊断准确率分别达到91.15%和96.27%,可实现对电路板故障的高准确率诊断。Objective A rapid and accurate detection of the fault occurring to the airborne electronic system plays a crucial role in ensuring the safety of civil aircraft.However,due to the increase of circuit board size and component density in airborne electronic system,the traditional contact fault diagnosis method encounters various problems such as low accuracy,huge time cost and the demanding requirements on personnel competency.Therefore,this study aims to explore the solution to circuit board fault diagnosis based on non-contact infrared technology,which is essential for improving the accuracy of fault diagnosis for the airborne electronic system.Methods After the sequential thermal image of the circuit board is captured by using the infrared camera,the region of interest in the thermal image is processed as the infrared temperature series.Since the infrared temperature series of the circuit board contains various fault-related information,the accuracy of fault diagnosis can be improved by making full use of its local and global features.In this study,a fault diagnosis algorithm is proposed to achieve this purpose.Composed of the features extraction network(FEN)and the relationship learning network(RLN),it utilizes the local features of temperature series and the relationship between the features.Built on a residual structure with multi-scale dilated CNN,FEN plays the role of a local-feature extraction network to construct a multi-scale receptive field without increasing the number of training parameters and to learn the spatial features of temperature series of different ranges.Based on the embedded structure of two identical layers,attention mechanism and LSTM network,RLN is a network that can apply control on the transmission of temperature series to learn the importance of features and assign attention weights for mining the correlations between the features extracted from different positions.To develop a complete circuit board fault diagnosis algorithm,the parallel FEN and RLN networks are connected to the"Soft

关 键 词：红外温度序列 电路故障诊断 多尺度膨胀卷积 长短期记忆网络 注意力机制 

分 类 号：TN21[电子电信—物理电子学]