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
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DOI：10.16652/j.issn.1004-373x.2026.10.006
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
中图分类号：TN911.23-34；TP18；TU998.1
文献标识码：A
文章编号：1004-373X（2026）10-0037-07
Fire pipeline network cross-domain fault diagnosis based on label-guided multi-scale adaptive feature contrast
Wen Chuanghui 1， Zhao Shuangyao 1，2，3， Zhang Xingze 1， Jin Xinyu 1， Cai Zhengyang 1，2，3
（1. School of Management， Hefei University of Technology， Hefei ， China;
2. Key Laboratory of Process Optimization and Intelligent Decision Making， Ministry of Education， Hefei ， China;
3. National Local Joint Engineering Research Center for Intelligent Decision making and Information System Technology， Hefei ， China）
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.
Keywords： fire pipeline network; fault diagnosis; small - sample learning; cross - domain learning; multi - scale attention mechanism; feature comparison
0 引言
消防管网作为城市消防安全体系的核心设施，其可靠性直接影响火灾初期的控火能力与人员疏散效率。（剩余9022字）