现代电子技术2026，Vol.49Issue(10)：37-43,7.DOI:10.16652/j.issn.1004-373x.2026.10.006
标签引导多尺度自适应特征对比的消防管网跨域故障诊断
Fire pipeline network cross-domain fault diagnosis based on label-guided multi-scale adaptive feature contrast
摘要
Abstract
As an important part of urban infrastructure,the fire protection pipe network faces dual challenges of small samples and cross-domain in its fault diagnosis.Traditional fault diagnosis methods usually suffer from problems such as insufficient generalization performance and poor model adaptability when dealing with cross-domain tasks with large differences in data distribution.After constructing a pipeline network fault dataset covering different leakage degrees and leakage locations,a cross-domain fault diagnosis method based on label-guided characteristic analysis and multi-scale attention mechanisms(LCA-MSA)is proposed.In this method,a learning strategy that combines multi-task learning with label-guided feature contrast is adopted,and the multi-scale convolution and attention mechanisms are introduced simultaneously,which enhances the model's ability to extract multi-level fault features.The experimental results demonstrate that the LCA-MSA model exhibits significant advantages in small-sample cross-domain fault diagnosis tasks for fire pipeline networks,achieving a diagnostic accuracy of 95.16%on the target domain test set.In comparison with traditional transfer learning and contrastive learning methods,the proposed method can show superior performance and better adaptability in fire pipeline fault diagnosis scenarios.关键词
消防管网/故障诊断/小样本学习/跨域学习/多尺度注意力机制/特征对比Key words
fire pipeline network/fault diagnosis/small-sample learning/cross-domain learning/multi-scale attention mechanism/feature comparison分类
信息技术与安全科学引用本文复制引用
温创辉,赵爽耀,张星泽,金鑫宇,蔡正阳..标签引导多尺度自适应特征对比的消防管网跨域故障诊断[J].现代电子技术,2026,49(10):37-43,7.基金项目
国家自然科学基金青年项目(72201087) （72201087）
中央高校经费项目(JZ2023HGTB0283) （JZ2023HGTB0283）
