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Early Detection and Reconstruction of Abnormal Data Using Hybrid VAE-LSTM Framework

  • Fangli Hou
  • Jun Ma
  • Jack C. P. Cheng
  • Helen H.L. Kwok

Early failure detection and abnormal data reconstruction in sensor data provided by building ventilation control systems are critical for public health. Early detection of abnormal data can help prevent failures in crucial components of ventilation systems, which can result in a variety of issues, from energy wastage to catastrophic outcomes. However, conventional fault detection models ignore valuable features of dynamic fluctuations in indoor air quality (IAQ) measurements and early warning signals of faulty sensor data. This study introduces a hybrid framework for early failure detection and abnormal data reconstruction applying variance analysis and variational autoencoders (VAE) coupled with the long short-term memory network (VAE-LSTM). The periodicity and stable fluctuation of IAQ data are exploited by variance analysis to detect unusual variations before failure occurs. The IAQ dataset which is corrupted by introducing complete failure, bias failure and precision degradation fault is then used to verify the feasibility of the VAE-LSTM model. The results of variance analysis reveal that unusual behavior of the data can be detected as early as 12 hours before failure occurs. The reconstruction performance of the developed method is shown to be superior to other methods under different abnormal data scenarios

  • Keywords:
  • Early failure detection,
  • Abnormal data reconstruction,
  • Variational autoencoder (VAE),
  • Long short-term memory network (LSTM),
  • Sustainable IAQ management,
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Fangli Hou

The Hong Kong University of Science and Technology, Hong Kong - ORCID: 0000-0002-5344-2852

Jun Ma

The University of Hong Kong, Hong Kong - ORCID: 0000-0001-9441-0083

Jack C. P. Cheng

University of Hong KongThe Hong Kong University of Science and Technology, Hong Kong - ORCID: 0000-0002-1722-2617

Helen H.L. Kwok

The Hong Kong University of Science and Technology, Hong Kong - ORCID: 0000-0002-7179-9281

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  • Publication Year: 2023
  • Pages: 933-942

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  • Publication Year: 2023

Chapter Information

Chapter Title

Early Detection and Reconstruction of Abnormal Data Using Hybrid VAE-LSTM Framework

Authors

Fangli Hou, Jun Ma, Jack C. P. Cheng, Helen H.L. Kwok

DOI

10.36253/979-12-215-0289-3.93

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Bibliographic Information

Book Title

CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality

Book Subtitle

Managing the Digital Transformation of Construction Industry

Editors

Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Publisher Name

Firenze University Press

DOI

10.36253/979-12-215-0289-3

eISBN (pdf)

979-12-215-0289-3

eISBN (xml)

979-12-215-0257-2

Series Title

Proceedings e report

Series ISSN

2704-601X

Series E-ISSN

2704-5846

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