基于多尺度域生成网络的冷水机组故障诊断
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关键词：冷水机组；单域泛化；故障诊断；域对抗；度量学习
中图分类号：TP277
DOI：10.3969/j.issn.1004-132X.2026.03.018 开放科学（资源服务）标识码（OSID）：
Abstract： To address the issues that domain generalization methods relied on data from multiple source domains for model training，while obtaining multi-operating condition data for chiller units was challenging，a fault diagnosis method was proposed for chiller units based on multi-scale domain generative network （MSDGN）. First，a multi-scale encoder-decoder convolutional neural network was used to extract multi-scale features from source domain data，and learnable weight parameters were introduced to dynamicall adjust the importance offeatures at each scale to enhance the diversity of the extended domain.Then， focal loss was applied to strengthen the penalty for semantically inconsistent samples，improving the semantic consistency of the extended domain.A combination of reverse metric learning strategies and a domain classifier was used to maximize the distribution diference between sources and extended domains，thereby achieving diversity in the training data. Finally，a domain adversarial strategy was employed to extract domain-invariant features from both the source and extended domains，and a triplet loss was introduced to minimize the distribution diference across multiple domains，enabling fault diagnosis for unknown operating conditions. By generating the extended domain，the model's fault diagnosis performance was improved under unknown conditions. The proposed method was experimentally validated using ASHRAE 1043-RP dataset and a metro dataset from a certain city. The results on ASHRAE lO43-RP dataset demonstrate that the proposed method effctively identifies faults even when target operating conditions are unseen， achieving a maximum diagnosis accuracy of 98.19% . Results on the metro dataset indicate that the proposed method exhibits practical applicability in real-world scenarios. Compared with existing methods，the proposed approach achieves superior fault diagnosis performance.
Key words： chiller;single domain generalization； fault diagnosis; domain adversarial; metric learning
0 引言
冷水机组的能耗占建筑总能耗的 40%[1] ，其故障运行会导致额外 15%～30% 的能源消耗[2]，造成大量的能源浪费，因此，针对冷水机组进行故障诊断研究具有重要意义。（剩余14685字）
