Contained in:
Book Chapter

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,
+ Show More

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

  1. Ali, A., & Dağtekin, R. (2008). Early warning signals of the 2000/2001 Turkish financial crisis. International Journal of Emerging and Transition Economies, 1(2), 191-218.
  2. Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000). LOF: identifying density-based local outliers. Paper presented at the Proceedings of the 2000 ACM SIGMOD international conference on Management of data.
  3. Bu, J., Liu, Y., Zhang, S., Meng, W., Liu, Q., Zhu, X., & Pei, D. (2018). Rapid deployment of anomaly detection models for large number of emerging kpi streams. Paper presented at the 2018 IEEE 37th International Performance Computing and Communications Conference (IPCCC).
  4. Cao, V. L., Nicolau, M., & McDermott, J. (2016). One-class classification for anomaly detection with kernel density estimation and genetic programming. Paper presented at the Genetic Programming: 19th European Conference, EuroGP 2016, Porto, Portugal, March 30-April 1, 2016, Proceedings 19.
  5. Drake, J. M., & Griffen, B. D. (2010). Early warning signals of extinction in deteriorating environments. Nature, 467(7314), 456-459.
  6. EPA. (2009). Indoor Air Quality Tools for Schools. Retrieved from https://www.epa.gov/iaq-schools/indoor-air-quality-tools-schools-action-kit
  7. Erfani, S. M., Rajasegarar, S., Karunasekera, S., & Leckie, C. (2016). High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning. Pattern Recognition, 58, 121-134.
  8. Hussain, S. N., Abd Aziz, A., Hossen, M. J., Ab Aziz, N. A., Murthy, G. R., & Mustakim, F. B. (2022). A novel framework based on cnn-lstm neural network for prediction of missing values in electricity consumption time-series datasets. Journal of Information Processing Systems, 18(1), 115-129.
  9. Kasam, A. A., Lee, B. D., & Paredis, C. J. (2014). Statistical methods for interpolating missing meteorological data for use in building simulation. Paper presented at the Building Simulation.
  10. Kim, M., Liu, H., Kim, J. T., & Yoo, C. (2014). Evaluation of passenger health risk assessment of sustainable indoor air quality monitoring in metro systems based on a non-Gaussian dynamic sensor validation method. Journal of hazardous materials, 278, 124-133.
  11. Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
  12. Kingma, D. P., & Welling, M. (2013). Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114.
  13. Lee, W.-Y., House, J. M., Park, C., & Kelly, G. E. (1996). Fault diagnosis of an air-handling unit using artificial neural networks. Transactions-American society of heating refrigerating and air conditioning engineers, 102, 540-549.
  14. Li, D., Zhou, Y., Hu, G., & Spanos, C. J. (2016). Fault detection and diagnosis for building cooling system with a tree-structured learning method. Energy and buildings, 127, 540-551.
  15. Li, X., Peng, L., Yao, X., Cui, S., Hu, Y., You, C., & Chi, T. (2017). Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation. Environmental pollution, 231, 997-1004.
  16. Lu, Y.-C., Shen, C.-H., & Wei, Y.-C. (2013). Revisiting early warning signals of corporate credit default using linguistic analysis. Pacific-Basin Finance Journal, 24, 1-21.
  17. Ma, J., Cheng, J. C., Jiang, F., Chen, W., Wang, M., & Zhai, C. (2020). A bi-directional missing data imputation scheme based on LSTM and transfer learning for building energy data. Energy and buildings, 216, 109941.
  18. Mesa-Jiménez, J. J., Stokes, L., Yang, Q., & Livina, V. (2021). Early warning signals of failures in building management systems. International Journal of Metrology and Quality Engineering, 12, 11.
  19. Ouyang, T., Zha, X., & Qin, L. (2017). A combined multivariate model for wind power prediction. Energy conversion and management, 144, 361-373.
  20. Qing, X., & Niu, Y. (2018). Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM. Energy, 148, 461-468.
  21. Rogers, B. M., Solvik, K., Hogg, E. H., Ju, J., Masek, J. G., Michaelian, M., . . . Goetz, S. J. (2018). Detecting early warning signals of tree mortality in boreal North America using multiscale satellite data. Global change biology, 24(6), 2284-2304.
  22. Su, Y., & Kuo, C.-C. J. (2019). On extended long short-term memory and dependent bidirectional recurrent neural network. Neurocomputing, 356, 151-161.
  23. Szabados, M., Kakucs, R., Páldy, A., Kotlík, B., Kazmarová, H., Dongiovanni, A., . . . Kukec, A. (2022). Association of parent-reported health symptoms with indoor air quality in primary school buildings–the InAirQ study. Building and Environment, 221, 109339.
  24. Wang, H., Feng, D., & Liu, K. (2021). Fault detection and diagnosis for multiple faults of VAV terminals using self-adaptive model and layered random forest. Building and Environment, 193, 107667.
  25. Wen, J., & Gao, H. (2018). Degradation assessment for the ball screw with variational autoencoder and kernel density estimation. Advances in Mechanical Engineering, 10(9), 1687814018797261.
  26. Yan, K., Zhong, C., Ji, Z., & Huang, J. (2018). Semi-supervised learning for early detection and diagnosis of various air handling unit faults. Energy and buildings, 181, 75-83. DOI: 10.1016/j.enbuild.2018.10.016
  27. Yu, Y., Woradechjumroen, D., & Yu, D. (2014). A review of fault detection and diagnosis methodologies on air-handling units. Energy and buildings, 82, 550-562.
  28. Zhao, Y., Li, T., Zhang, X., & Zhang, C. (2019). Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future. Renewable and Sustainable Energy Reviews, 109, 85-101.
PDF
  • Publication Year: 2023
  • Pages: 933-942

XML
  • 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

31

Fulltext
downloads

36

Views

Export Citation

1,307

Open Access Books

in the Catalogue

1,949

Book Chapters

3,290,448

Fulltext
downloads

4,134

Authors

from 860 Research Institutions

of 63 Nations

63

scientific boards

from 339 Research Institutions

of 43 Nations

1,150

Referees

from 345 Research Institutions

of 37 Nations