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Research Article

Leveraging graph convolutional networks for semi-supervised fault diagnosis of HVAC systems in data-scarce contexts

Cheng Fan1,2,3, Yiwen Lin2,3, Marco Savino Piscitelli4, Roberto Chiosa4, Huilong Wang1,2,3( ), Alfonso Capozzoli4, Yuanyuan Ma2,3
Key Laboratory for Resilient Infrastructures of Coastal Cities, Ministry of Education, Shenzhen University, Shenzhen, China
Sino-Australia Joint Research Center in BIM and Smart Construction, Shenzhen University, Shenzhen, China
College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, China
Department of Energy, TEBE research group, BAEDA Lab, Politecnico di Torino, Torino, Italy
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Abstract

The continuous accumulation of operational data has provided an ideal platform to devise and implement customized data analytics for smart HVAC fault detection and diagnosis. In practice, the potentials of advanced supervised learning algorithms have not been fully realized due to the lack of sufficient labeled data. To tackle such data challenges, this study proposes a graph neural network-based approach to effectively utilizing both labeled and unlabeled operational data for optimum decision-makings. More specifically, a graph generation method is proposed to transform tabular building operational data into association graphs, based on which graph convolutions are performed to derive useful insights for fault classifications. Data experiments have been designed to evaluate the values of the methods proposed. Three datasets on HVAC air-side operations have been used to ensure the generalizability of results obtained. Different data scenarios, which vary in training data amounts and imbalance ratios, have been created to comprehensively quantify behavioral patterns of representative graph convolution networks and their architectures. The research results indicate that graph neural networks can effectively leverage associations among labeled and unlabeled data samples to achieve an increase of 2.86%–7.30% in fault classification accuracies, providing a novel and promising solution for smart building management.

Keywords

machine learningsemi-supervised learningHVAC systemsfault detection and diagnosisgraph convolutional networks

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Building Simulation
Volume 16 Issue 8,
August 2023
Pages 1499-1517
DOI: 10.1007/s12273-023-1041-1
Cite this article:
Fan C, Lin Y, Piscitelli MS, et al. Leveraging graph convolutional networks for semi-supervised fault diagnosis of HVAC systems in data-scarce contexts. Building Simulation, 2023, 16(8): 1499-1517. https://doi.org/10.1007/s12273-023-1041-1

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Received: 19 March 2023
Revised: 17 April 2023
Accepted: 07 May 2023
Published: 21 June 2023
© Tsinghua University Press 2023
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