Unsupervised Anomaly Detection and Localization for Multivariate Time Series and Their Applications in Structural Health Monitoring
This thesis advances the field of anomaly detection in multivariate time series by addressing key challenges in anomaly detection, localization, and severity assessment. Through the development of EdgeConvFormer and U-GraphFormer, this research offers robust, interpretable, and efficient solutions a...
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| Format: | Thesis |
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Curtin University
2024
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| Online Access: | http://hdl.handle.net/20.500.11937/96603 |
| _version_ | 1848766176503529472 |
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| author | Liu, Jie |
| author_facet | Liu, Jie |
| author_sort | Liu, Jie |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This thesis advances the field of anomaly detection in multivariate time series by addressing key challenges in anomaly detection, localization, and severity assessment. Through the development of EdgeConvFormer and U-GraphFormer, this research offers robust, interpretable, and efficient solutions applicable across various domains, with a particular focus on SHM. Extensive evaluations across diverse multivariate time series datasets and real-world scenarios demonstrate the potential of these models to enhance the monitoring and maintenance of critical systems, ensuring their safety and longevity. |
| first_indexed | 2025-11-14T11:46:59Z |
| format | Thesis |
| id | curtin-20.500.11937-96603 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:46:59Z |
| publishDate | 2024 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-966032024-12-17T00:20:24Z Unsupervised Anomaly Detection and Localization for Multivariate Time Series and Their Applications in Structural Health Monitoring Liu, Jie This thesis advances the field of anomaly detection in multivariate time series by addressing key challenges in anomaly detection, localization, and severity assessment. Through the development of EdgeConvFormer and U-GraphFormer, this research offers robust, interpretable, and efficient solutions applicable across various domains, with a particular focus on SHM. Extensive evaluations across diverse multivariate time series datasets and real-world scenarios demonstrate the potential of these models to enhance the monitoring and maintenance of critical systems, ensuring their safety and longevity. 2024 Thesis http://hdl.handle.net/20.500.11937/96603 Curtin University restricted |
| spellingShingle | Liu, Jie Unsupervised Anomaly Detection and Localization for Multivariate Time Series and Their Applications in Structural Health Monitoring |
| title | Unsupervised Anomaly Detection and Localization for
Multivariate Time Series and Their Applications in
Structural Health Monitoring |
| title_full | Unsupervised Anomaly Detection and Localization for
Multivariate Time Series and Their Applications in
Structural Health Monitoring |
| title_fullStr | Unsupervised Anomaly Detection and Localization for
Multivariate Time Series and Their Applications in
Structural Health Monitoring |
| title_full_unstemmed | Unsupervised Anomaly Detection and Localization for
Multivariate Time Series and Their Applications in
Structural Health Monitoring |
| title_short | Unsupervised Anomaly Detection and Localization for
Multivariate Time Series and Their Applications in
Structural Health Monitoring |
| title_sort | unsupervised anomaly detection and localization for
multivariate time series and their applications in
structural health monitoring |
| url | http://hdl.handle.net/20.500.11937/96603 |