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 |