Expert condition monitoring on hydrostatic self-levitating bearings
Neural network based functional approximation techniques associated with rule based techniques are applied on the condition monitoring task of rotating machines equipped with hydrostatic self levitating bearings. Based on fluid online measured characteristic data, including pressures and temperature, the inherent hydraulic pumping system and the self levitating shaft is monitored and diagnosed applying vibration analysis carried out using virtual measurements. Required signals are achieved by conversion of measured data (fluid temperatures and pressures) into virtual data (vibration magnitudes) by means of neural network functional approximation techniques. Previous to the condition monitoring task (vibration analysis), a supervision task of the system behaviour is carried out in order to validate the information being processed. It is concluded that the vibration analysis based on the analysis of the dynamic behaviour of oil pressure (non accelerometer based signals) subjected to disturbances such as changes in oil operating conditions including viscosity, is successfully feasible.
condition monitoring functional approximation vibration analysis virtual data fault diagnosis feedforward neural networks