A graph embedding based fault detection framework for process systems with multi-variate time-series datasets

Due to the enormous potential of modelling, graph-based approaches have been used for various applications in the process industries. In this study, we propose a fault detection framework through graphs by utilising its attributes in the form of node embeddings. Shallow embedding methods are deploye...

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Главные авторы:	Umang Goswami, Jyoti Rani, Hariprasad Kodamana, Prakash Kumar Tamboli, Parshotam Dholandas Vaswani
Формат:	Artigo
Язык:	Inglês
Опубликовано:	Elsevier 2024-03-01
Серии:	Digital Chemical Engineering
Предметы:	Graph machine learning Fault detection Embeddings Variational auto-encoder Reconstruction loss
Online-ссылка:	http://www.sciencedirect.com/science/article/pii/S2772508123000534
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