Digital Twin-Based Monitoring System of Induction Motors Using IoT Sensors and Thermo-Magnetic Finite Element Analysis

Digital Twin-Based Monitoring System of Induction Motors Using IoT Sensors and Thermo-Magnetic Finite Element Analysis

Electric induction motors are the type of motor most commonly operated in industry, and for this reason technologies that predict faults and reduce the corrective maintenance are of great interest. In this context, this paper presents a predictive maintenance tool of electric motors using the concep...

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Bibliographic Details
Published in:IEEE access Vol. 11; pp. 1682 - 1693
Main Authors: Santos, Jhennifer F. Dos, Tshoombe, Bendict K., Santos, Lucas H. B., Araujo, Ramon C. F., Manito, Allan R. A., Fonseca, Wellington S., Silva, Marcelo O.
Format: Journal Article
Language:English
Published: Piscataway IEEE 2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Computer simulation
Condition monitoring
Data models
digital twin
Digital twins
Electric motors
Errors
finite element analysis
Finite element method
Induction motors
Industrial applications
Internet of Things
Magnetic flux
Magnetic induction
Monitoring
Numerical models
Predictive maintenance
Sensors
Temperature measurement
Temperature sensors
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Summary:Electric induction motors are the type of motor most commonly operated in industry, and for this reason technologies that predict faults and reduce the corrective maintenance are of great interest. In this context, this paper presents a predictive maintenance tool of electric motors using the concepts of Digital Twin (DT) and Industrial Internet of Things (IIoT). The proposed system is innovative, as it monitors the motor current and temperature by means of sensors and a low-cost acquisition module, and these measurements are sent via Wi-Fi to a database. The concept of DT was leveraged by providing the measurements as inputs to a high-fidelity strongly-coupled model of the monitored monitor, using the Finite Element Method (FEM). The results obtained are satisfactory, because the sensors used presented acceptable errors that do not interfere with the reliability of the results. The computer simulation showed relative errors below 4% in the conductivity analysis and 10% in the temperature analysis. In addition, the simulation allows verifying the internal temperature of the motor, its resistive losses, and the intensity of the magnetic flux at each pole. It is worth pointing out that the internal analysis performed is only possible due to the combination of IIoT and computer simulations. Therefore, they allow a better diagnosis of the motor's operational status and also a time estimate for the next maintenance service, thus being ideal for the industrial sector.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3232063