Unsupervised Anomaly Detection and Localization for
Multivariate Time Series and Their Applications in
Structural Health Monitoring

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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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Bibliographic Details
Main Author:	Liu, Jie
Format:	Thesis
Published:	Curtin University 2024
Online Access:	http://hdl.handle.net/20.500.11937/96603

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