Flotation Process Fault Detection and Isolation using Neural ODE for generation of vector-field features

Luis Enciso-Salas, Gustavo Pérez-Zuñiga, Javier Sotomayor-Moriano

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva


Flotation in the mining industry is of vital importance for obtaining the right quality of product with efficiency and represents a critical process where possible failures must be monitored at all times. In this paper, complete fault detection and isolation system (FDI) based on the Neural Ordinary Differential Equations (NODE) framework is proposed; the NODE is employed to represent the dynamics of the studied plant based on the measured variables and inputs. Then, a classifier can be used to identify the faults based on the projections of the derivatives or local vector field generated by the NODE using the estimations and actual measurements. The proposed approach is applied to a controlled mining flotation process that has perturbations. The solution is compared with other known machine learning techniques showing better performance metrics. Moreover, it is demonstrated with t-SNE representation that features generated from the NODE model improve the classification.

Idioma originalInglés
Título de la publicación alojadaIFAC-PapersOnLine
EditoresHideaki Ishii, Yoshio Ebihara, Jun-ichi Imura, Masaki Yamakita
EditorialElsevier B.V.
Número de páginas6
ISBN (versión digital)9781713872344
EstadoPublicada - 1 jul. 2023
Evento22nd IFAC World Congress - Yokohama, Japón
Duración: 9 jul. 202314 jul. 2023

Serie de la publicación

ISSN (versión digital)2405-8963


Conferencia22nd IFAC World Congress


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