Neural network model of mill-fan system elements vibration for predictive maintenance

In the present paper we focus on online monitoring system for predictive maintenance based on sensor automated inputs. Our subject was a device from Maritsa East 2 power plant — a mill fan. The main sensor information we have access to is based on the vibration of the nearest to the mill rotor bearing block. Our aim was to create a (nonlinear) model able to predict on time possible changes in vibrations tendencies that can be early signal for system work deterioration. For that purpose recently developed kind of Recurrent Neural Networks named Echo state networks were applied. The preliminary investigations showed their good approximation ability for our purpose. Direction of future work will be increasing of predications time horizon.