Chapter Early Detection and Reconstruction of Abnormal Data Using Hybrid VAE-LSTM Framework
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, f...
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| स्वरूप: | Online |
| भाषा: | अंग्रेज़ी |
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Firenze University Press
2024
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| ऑनलाइन पहुंच: | ONIX_20240402_9791221502893_8 |
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| _version_ | 1869526575610855424 |
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| author | Hou, Fangli Ma, Jun Cheng, Jack C. P. Kwok, Helen H.L. |
| author_browse | Cheng, Jack C. P. Hou, Fangli Kwok, Helen H.L. Ma, Jun |
| author_facet | Hou, Fangli Ma, Jun Cheng, Jack C. P. Kwok, Helen H.L. |
| author_sort | Hou, Fangli |
| collection | Directory of Open Access Books |
| description | 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 |
| format | Online |
| id | doab-20.500.12854ir-136084 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Firenze University Press |
| publisherStr | Firenze University Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1360842025-07-18T09:46:45Z Chapter Early Detection and Reconstruction of Abnormal Data Using Hybrid VAE-LSTM Framework Hou, Fangli Ma, Jun Cheng, Jack C. P. Kwok, Helen H.L. Early failure detection Abnormal data reconstruction Variational autoencoder (VAE) Long short-term memory network (LSTM) Sustainable IAQ management thema EDItEUR::U Computing and Information Technology thema EDItEUR::U Computing and Information Technology 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 2024-04-04T04:15:30Z 2024-04-04T04:15:30Z 2024-04-02T15:44:27Z 2023 chapter ONIX_20240402_9791221502893_8 2704-5846 https://library.oapen.org/handle/20.500.12657/89039 9791221502893 https://directory.doabooks.org/handle/20.500.12854/136084 eng Proceedings e report open access image/jpeg n/a https://library.oapen.org/bitstream/20.500.12657/89039/1/9791221502893_93.pdf Firenze University Press 10.36253/979-12-215-0289-3.93 10.36253/979-12-215-0289-3.93 2ec4474d-93b1-4cfa-b313-9c6019b51b1a 9791221502893 10 Florence open access |
| spellingShingle | Early failure detection Abnormal data reconstruction Variational autoencoder (VAE) Long short-term memory network (LSTM) Sustainable IAQ management thema EDItEUR::U Computing and Information Technology thema EDItEUR::U Computing and Information Technology Hou, Fangli Ma, Jun Cheng, Jack C. P. Kwok, Helen H.L. Chapter Early Detection and Reconstruction of Abnormal Data Using Hybrid VAE-LSTM Framework |
| title | Chapter Early Detection and Reconstruction of Abnormal Data Using Hybrid VAE-LSTM Framework |
| title_full | Chapter Early Detection and Reconstruction of Abnormal Data Using Hybrid VAE-LSTM Framework |
| title_fullStr | Chapter Early Detection and Reconstruction of Abnormal Data Using Hybrid VAE-LSTM Framework |
| title_full_unstemmed | Chapter Early Detection and Reconstruction of Abnormal Data Using Hybrid VAE-LSTM Framework |
| title_short | Chapter Early Detection and Reconstruction of Abnormal Data Using Hybrid VAE-LSTM Framework |
| title_sort | chapter early detection and reconstruction of abnormal data using hybrid vae lstm framework |
| topic | Early failure detection Abnormal data reconstruction Variational autoencoder (VAE) Long short-term memory network (LSTM) Sustainable IAQ management thema EDItEUR::U Computing and Information Technology thema EDItEUR::U Computing and Information Technology |
| topic_facet | Early failure detection Abnormal data reconstruction Variational autoencoder (VAE) Long short-term memory network (LSTM) Sustainable IAQ management thema EDItEUR::U Computing and Information Technology thema EDItEUR::U Computing and Information Technology |
| url | ONIX_20240402_9791221502893_8 |
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