IRACI MARTINEZ PEREIRA GONCALVES

IRACI MARTINEZ PEREIRA GONCALVES

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Monitoring system for an experimental facility using GMDH methodology
2021 - PEREIRA, IRACI M.; MORAES, DAVI A.
This work presents a Monitoring System based on the GMDH - Group Method of Data Handling methodology applied in an Experimental Test Facility. GMDH is a combinatorial multi-layer algorithm in which a network of layers and nodes is generated using a number of inputs from the data stream being evaluated. The GMDH method is based on an underlying assumption that the data can be modeled by using an approximation of the Volterra Series or Kolmorgorov-Gabor polynomial. The Fault Test Experimental Facility was designed inspired in a PWR nuclear power plant and is composed by elements that correspond to the pressure vessel, steam generator, pumps of the primary and secondary reactor loops. The nuclear reactor core is represented by an electrical heater with different values of power. The exper-imental plant is fully instrumented with sensors and actuators. The Fault Test Experimental Facility can be operated to generate normal and faulty data. These failures can be added initially with small magnitude, and their magnitude being increasing gradually in a controlled way. The database will interface with the plant supervisory system SCADA (Super-visory Control and Data Acquisition) that provides the data through standard interface.
Monitoring system for an experimental facility using GMDH methodology
2017 - PEREIRA, IRACI M.; MORAES, DAVI A.; BUENO, ELAINE I.
This work presents a Monitoring System developed based on the GMDH - Group Method of Data Handling methodology to be used in an Experimental Test Facility. GMDH is a combinatorial multi-layer algorithm in which a network of layers and nodes is generated using a number of inputs from the data stream being evaluated. The GMDH network topology has been traditionally determined using a layer by layer pruning process based on a pre-selected criterion of what constitutes the best nodes at each level. The traditional GMDH method is based on an underlying assumption that the data can be modeled by using an approximation of the Volterra Series or Kolmorgorov-Gabor polynomial. The Fault Test Experimental Facility was designed to simulate a PWR nuclear power plant and is composed by elements that correspond to the pressure vessel, steam generator, pumps of the primary and secondary reactor loops. The nuclear reactor core is represented by an electrical heater with different values of power. The experimental plant will be fully instrumented with sensors and actuators, and the data acquisition system will be constructed in order to enable the details of the temporal analysis of process variables. The Fault Test Experimental Facility can be operated to generate normal and fault data. These failures can be added initially with small magnitude, and their magnitude being increasing gradually in a controlled way. The database will interface with the plant supervisory system SCADA (Supervisory Control and Data Acquisition) that provides the data through standard interface.
Neural networks used to monitor an experimental test workbench
2017 - MORAES, DAVI A.; PEREIRA, IRACI M.
This work presents the application of neural networks in an experimental workbench. This bench was developed with the purpose of conducting real time tests and data acquisition. The method applied for this work allowed to generate faulty data in a gradual and controlled way through the binary combination of double action valves. Using the SCADA application (Supervisory Control and Data Acquisition), it became possible to acquire data for analysis in Matlab / Simulink software. This bench has two reservoirs: a reservoir that has sensors for recording pressure and temperature variables for later analysis, and another reservoir that has level sensors. Four models were used to develop the respective practical experiments. In the first model, it was possible to perform all practical tests of the plant, as well as mechanical changes like repositioning of some mechanical components, piping, sensors and electrovalves. In the second model, it was noticed that the positioning of the flow meter, located after the pump output, prevented a good measurement of the flow variable. In the third model, it was perceived that the number of failures initially adopted, made the data too confusing for the neural network analysis. In the last model, it was possible to obtain a performance of 96.6% of hits after the reconfiguration for 4 controlled faults.
Development of a fault test experimental facility model using MATLAB
2015 - PEREIRA, IRACI M.; MORAES, DAVI A.