INSUMOT

The general objective of the project is to develop an on-line system able to reliably determine, at every moment, the health of the insulation of an electric motor or generator. Moreover, the developed system must be able to determine the actual efficiency of the machine, as well as quantify the repercussions that the insulation degradation has in terms of machine efficiency reduction. To this end, the developed system will rely on monitoring different machine quantities. Some of them have shown promising results for the detection of insulation problems while other techniques (that have led to good results when diagnosing other faults) will be applied for the first time in the context of insulation health monitoring. The team members consider that merging these different technologies may provide a reliable diagnosis of the insulation condition since the possible gaps of a single method can be compensated with the others. More specifically, the quantities that will be evaluated and integrated in the final insulation monitoring system will be: partial discharges, currents, infrared data and external magnetic field. These basic quantities will be combined with two other alternatives that have recently emerged with promising results, such as capacitance and dissipation factor monitoring as well as impedance spectroscopy. Different modules will be developed, based on each technique. They will rely on intelligent diagnosis algorithms that will process the corresponding monitored quantity and will reach a diagnostic conclusion. These algorithms will be developed and optimized based on the data that will be obtained from experimental tests that will be carried out in the laboratory, as well as from field machines of different industries. The accuracy and reliability of each module will be properly assessed and the range of detectable insulation failures will be determined. Afterwards, the different modules will be merged in a single system that will combine all the information coming from the different modules, in order to obtain a reliable conclusion about the health of the insulation. Moreover, a special module aimed to quantify the impact of the insulation health on the machine efficiency will be developed and integrated in the final system. The final intelligent system will be finally validated with data coming from real electric motors and generators.

Duration: January 2019- December 2020 Principal Investigator: Prof. JOSE A ANTONINO DAVIU