Modeling, monitoring, and diagnosis of complex systems with high-dimensional streaming data
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Estrada Gomez, Ana Maria
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Abstract
With the development of technology, sensing systems became ubiquitous. As a result, a wide variety of complex systems are continuously monitored by hundreds of sensors collecting large volumes of rich data. Learning the structure of complex systems, from sensing data, provides unique opportunities for real-time process monitoring and for accurate fault diagnosis in a wide range of applications. This dissertation presents new methodologies to analyze the high-dimensional data collected by sensors to learn the interactions between different entities in complex systems for system monitoring and diagnosis.
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Date
2021-07-20
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Text
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Dissertation