The Experts below are selected from a list of 243 Experts worldwide ranked by ideXlab platform

Alexandre Balbinot - One of the best experts on this subject based on the ideXlab platform.

  • Virtual and Self-Validating Sensor for Speed Estimation of a DC Motor in a Prototype Plant
    IFSA Publishing S.L., 2015
    Co-Authors: Fernando Crivellaro, Gustavo Künzel, Alexandre Balbinot
    Abstract:

    In applications where the speed control of a motor may not be interrupted, even for maintenance of the speed sensor or speed sensor unavailability, the virtual sensing is a powerful tool. The proposed virtual speed sensor uses artificial neural networks to provide motor speed estimation when an encoder is not operating for this purpose. The artificial neural network uses motor load information, Armature current measured with a shunt resistor and Armature Applied Voltage as input parameters to provide the estimated speed. In parallel, self-validating sensor status is analyzed, coordinating the speed control actions for plant maintenance. For self-validating purposes, a known current flow is Applied in the shunt resistor to generate the sensor status. A microcontroller is responsible for control of the Applied Voltage in the DC motor and also for data acquisition. This microcontroller is slave of an application running in a personal computer, where all the information is processed to make control decisions and to alert the user accordingly with the previously artificial neural network train. The application presents the sensor status and the measured and estimated speed graphs, allowing the user to monitor and to analyze the plant behavior. Some fail situations were tested in a closed loop system, demonstrating the virtual sensor operation in these cases, including the sensor status detection and its correspondent control actions

Fernando Crivellaro - One of the best experts on this subject based on the ideXlab platform.

  • Virtual and Self-Validating Sensor for Speed Estimation of a DC Motor in a Prototype Plant
    IFSA Publishing S.L., 2015
    Co-Authors: Fernando Crivellaro, Gustavo Künzel, Alexandre Balbinot
    Abstract:

    In applications where the speed control of a motor may not be interrupted, even for maintenance of the speed sensor or speed sensor unavailability, the virtual sensing is a powerful tool. The proposed virtual speed sensor uses artificial neural networks to provide motor speed estimation when an encoder is not operating for this purpose. The artificial neural network uses motor load information, Armature current measured with a shunt resistor and Armature Applied Voltage as input parameters to provide the estimated speed. In parallel, self-validating sensor status is analyzed, coordinating the speed control actions for plant maintenance. For self-validating purposes, a known current flow is Applied in the shunt resistor to generate the sensor status. A microcontroller is responsible for control of the Applied Voltage in the DC motor and also for data acquisition. This microcontroller is slave of an application running in a personal computer, where all the information is processed to make control decisions and to alert the user accordingly with the previously artificial neural network train. The application presents the sensor status and the measured and estimated speed graphs, allowing the user to monitor and to analyze the plant behavior. Some fail situations were tested in a closed loop system, demonstrating the virtual sensor operation in these cases, including the sensor status detection and its correspondent control actions

Gustavo Künzel - One of the best experts on this subject based on the ideXlab platform.

  • Virtual and Self-Validating Sensor for Speed Estimation of a DC Motor in a Prototype Plant
    IFSA Publishing S.L., 2015
    Co-Authors: Fernando Crivellaro, Gustavo Künzel, Alexandre Balbinot
    Abstract:

    In applications where the speed control of a motor may not be interrupted, even for maintenance of the speed sensor or speed sensor unavailability, the virtual sensing is a powerful tool. The proposed virtual speed sensor uses artificial neural networks to provide motor speed estimation when an encoder is not operating for this purpose. The artificial neural network uses motor load information, Armature current measured with a shunt resistor and Armature Applied Voltage as input parameters to provide the estimated speed. In parallel, self-validating sensor status is analyzed, coordinating the speed control actions for plant maintenance. For self-validating purposes, a known current flow is Applied in the shunt resistor to generate the sensor status. A microcontroller is responsible for control of the Applied Voltage in the DC motor and also for data acquisition. This microcontroller is slave of an application running in a personal computer, where all the information is processed to make control decisions and to alert the user accordingly with the previously artificial neural network train. The application presents the sensor status and the measured and estimated speed graphs, allowing the user to monitor and to analyze the plant behavior. Some fail situations were tested in a closed loop system, demonstrating the virtual sensor operation in these cases, including the sensor status detection and its correspondent control actions