市政技术2024，Vol.42Issue(5)：220-227,8.DOI:10.19922/j.1009-7767.2024.05.220
基于逐层互信息对抗自编码器的城市供热管网故障检测
Fault Detection of Urban Heating Pipe Networks Based on Layer-by-Layer Mutual Information Adversarial Auto-Encoder
摘要
Abstract
Being an important part of the municipal engineering network,safe and stable operation of urban central heating pipe network is closely related to the city's economic production and residents'daily life,so that it is crucial to conduct accurate and real-time condition monitoring of the heating pipe network.In recent years,deep learning-based methods have been widely used in the field of condition monitoring,such as adversarial auto-encoder(AAE).However,from the perspective of information theory,there is a decay of mutual information between samples and feature representations during the training process of AAE model,which directly affects the fault detection perfor-mance of the model.A fault detection method based on layer-by-layer mutual information(LM)AAE is proposed.By explicitly introducing mutual information between the low-dimensional feature space and each previous layer of the neural network and the correlation is maximum between normally input samples and feature representations,the mutual information decay problem is effectively overcome during AAE model training.Finally,LM-AAE model,VAE model and traditional AAE model are used for continuous stirred kettle-type heater experiments respectively.The results show that LM-AAE model has both the smallest fault false alarm rate and a smaller fault leakage rate.It is demonstrated that the introduction of a layer-by-layer mutual information strategy can make the model more su-perior in fault detection.关键词
供热管网/故障检测/无监督学习/对抗自编码器/逐层互信息Key words
heating pipe network/fault detection/unsupervised learning/adversarial auto-encoder(AAE)/layer-by-layer mutual information分类
建筑与水利引用本文复制引用
刘自鹏,李灵,刘述,李磊,熊凌云,刘雅儒..基于逐层互信息对抗自编码器的城市供热管网故障检测[J].市政技术,2024,42(5):220-227,8.基金项目
湖南省自然科学基金项目(2022JJ40510) （2022JJ40510）
湖南省教育厅科学研究项目(22B0329,21B0311) （22B0329,21B0311）
长沙理工大学校级研究生科研创新项目(CXCLY2022083) （CXCLY2022083）
