首页|An interpretable graph convolutional neural network based fault diagnosis method for building energy systems

An interpretable graph convolutional neural network based fault diagnosis method for building energy systems

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Due to the fast-modeling speed and high accuracy,deep learning has attracted great interest in the field of fault diagnosis in building energy systems in recent years.However,the black-box nature makes deep learning models generally difficult to interpret.In order to compensate for the poor interpretability of deep learning models,this study proposed a fault diagnosis method based on interpretable graph neural network(GNN)suitable for building energy systems.The method is developed by following three main steps:(1)selecting NC-GNN as a fault diagnosis model for building energy systems and proposing a graph generation method applicable to the model,(2)developing an interpretation method based on InputXGradient for the NC-GNN,which is capable of outputting the importance of the node features and automatically locating the fault related features,(3)visualizing the results of model interpretation and validating by matching with expert knowledge and maintenance experience.Validation was performed using the public ASHRAE RP-1043 chiller fault data.The diagnosis results show that the proposed method has a diagnosis accuracy of over 96％.The interpretation results show that the method is capable of explaining the decision-making process of the model by identifying fault-discriminative features.For almost all seven faults,their fault-discriminative features were correctly identified.

fault diagnosisgraph neural networkbuilding energy systemInputXGradientfeatureinterpretation

Guannan Li、Zhanpeng Yao、Liang Chen、Tao Li、Chengliang Xu

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School of Urban Construction,Wuhan University of Science and Technology,Wuhan 430065,China

Anhui Province Key Laboratory of Intelligent Building and Building Energy-saving,Anhui Jianzhu University,Hefei 230601,China

State Key Laboratory of Green Building in Western China,Xi'an University of Architecture & Technology,Xi'an 710055,China

Key Laboratory of Low-grade Energy Utilization Technologies and Systems(Chongqing University),Ministry of Education of China,Chongqing University,Chongqing 400044,China

Hubei Provincial Engineering Research Centre of Urban Regeneration,Wuhan University of Science and Technology,Wuhan 430081,China

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Opening Fund of Key Laboratory of Lowgrade Energy Utilization Technologies and Systems(Chongqing University)Ministry of Education of ChinaNational Natural Science Foundation of ChinaOpening Fund of State Key Laboratory of Green Building in Western Chinaopen Foundation of Anhui Province Key Laboratory of Intelligent Building and Building Energysaving"The 14th Five Year Plan"Hubei Provincial advantaged characteristic disciplines(groups)project of Wuhan University of Science anWuhan University of Science and Technology Postgraduate Innovation and Entrepreneurship Fund2021 Construction Technology Plan Project of Hubei Province

LLEUTS20230551906181LSKF202316IBES2022KF112023D0504JCX20220162021-83

2024

10.1007/s12273-024-1125-6

建筑模拟(英文版)

建筑模拟(英文版)

EI
ISSN：1996-3599
年,卷(期)：2024.17(7)