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

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.