实复域多尺度多层次融合的复合故障定位方法  

Compound fault location algorithm based on multi-scale and multi-layerfeature fusion in real domain and complex domain

作　　者：宋佳宇 高雪莲[1] 陈哲煊 Song Jiayu;Gao Xuelian;Chen Zhexuan(School of Electrical and Electronic Engineering,North China Electric Power University,Beijing 102206,China)

机构地区：[1]华北电力大学电气与电子工程学院,北京102206

出　　处：《电子测量技术》2025年第5期118-127,共10页Electronic Measurement Technology

摘　　要：针对模拟电路多故障并发的定位问题,提出了一种实复域相结合的定位算法。在实数域利用空间和通道注意力机制的不同操作,在控制网络深度和参数数量的同时获取完整数据特征;在复数域利用复数卷积神经网络跨越层级产生的差异,通过跳跃联接构建深浅层特征融合结构,保留了易丢失的浅层信息并将其与深层信息融合后得到复数域特征。将实复域特征融合用于模拟电路复合故障定位研究,定位平均准确率均在85%以上,最高准确率达到100%。该方法具有较强的稳定性和鲁棒性,为模拟电路复合故障定位研究提供了可行性方案。To tackle the challenge of multiple concurrent fault localization in analog circuits,we propose a localization algorithm that integrates both real and complex domains.In the real domain,comprehensive data features are extracted by employing various operations of spatial and channel attention mechanisms,while simultaneously managing network depth and parameter amount.Within the complex domain,we capitalize on the disparities emerging across layers within a complex-valued convolutional neural network.Through skip connections,a deep-shallow feature fusion structure is formulated,ensuring that shallow information susceptible to loss is preserved and integrated with deep information to yield complex-domain features.The integration of real and complex domain features is subsequently applied to the research on composite fault localization in analog circuits,yielding an average localization accuracy exceeding 93%and a peak accuracy reaching 100%.This approach demonstrates robust stability and reliability,furnishing a viable solution for the ongoing research on composite fault localization in analog circuits.

关 键 词：复合故障定位 注意力机制 复数卷积 特征融合 

分 类 号：TH407[机械工程—机械制造及自动化] TN37[电子电信—物理电子学]