Viscosity Measurement Monitoring by Means of Functional Approximation and Rule Based Techniques

A diagnosis strategy using neural network based functional approximation models associated to a rule based technique is developed. The aim is to apply the diagnostic task on condition monitoring of viscometers used in liquids handling (liquid fuels and lubricating oils) tasks. Based on fluid online measured data, including pressures and temperature, the viscometers diagnosis is being carried out. Required signals are achieved by conversion of available or measured data (fluid temperature and API and SAE grades) into virtual data by means of neural network functional approximation techniques. Using rule based techniques on fault finding and isolation task, it is concluded that the viscometer monitoring task carried out by the analysis of the dynamic behaviour of both, the on line viscometer and virtual data subjected to the analysis of residuals into a parity space approach, is successfully feasible.

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