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...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9998505/ |
| _version_ | 1797959597960462336 |
|---|---|
| author | Jhennifer F. Dos Santos Bendict K. Tshoombe Lucas H. B. Santos Ramon C. F. Araujo Allan R. A. Manito Wellington S. Fonseca Marcelo O. Silva |
| author_facet | Jhennifer F. Dos Santos Bendict K. Tshoombe Lucas H. B. Santos Ramon C. F. Araujo Allan R. A. Manito Wellington S. Fonseca Marcelo O. Silva |
| author_sort | Jhennifer F. Dos Santos |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T00:34:58Z |
| format | Article |
| id | doaj.art-7523013a7106499ab8810d9034e80f90 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T00:34:58Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7523013a7106499ab8810d9034e80f902023-01-07T00:00:24ZengIEEEIEEE Access2169-35362023-01-01111682169310.1109/ACCESS.2022.32320639998505Digital Twin-Based Monitoring System of Induction Motors Using IoT Sensors and Thermo-Magnetic Finite Element AnalysisJhennifer F. Dos Santos0https://orcid.org/0000-0001-9716-5669Bendict K. Tshoombe1https://orcid.org/0000-0002-1478-4227Lucas H. B. Santos2https://orcid.org/0000-0002-2732-350XRamon C. F. Araujo3https://orcid.org/0000-0002-3786-801XAllan R. A. Manito4https://orcid.org/0000-0002-4550-9527Wellington S. Fonseca5https://orcid.org/0000-0002-2602-1964Marcelo O. Silva6https://orcid.org/0000-0001-9319-4438Department of Electrical Engineering, Federal University of Pará, Belém, BrazilDepartment of Electrical Engineering, Federal University of Pará, Belém, BrazilDepartment of Electrical Engineering, Federal University of Pará, Belém, BrazilDepartment of Theoretical and Experimental Physics, Federal University of Rio Grande do Norte, Natal, BrazilElectrical Engineering Graduate Program, Federal University of Pará, Belém, BrazilDepartment of Electrical Engineering, Federal University of Pará, Belém, BrazilMechanical Engineering Graduate Program, Federal University of Pará, Belém, BrazilElectric 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.https://ieeexplore.ieee.org/document/9998505/Condition monitoringdigital twinfinite element analysisinduction motorsInternet of Things |
| spellingShingle | Jhennifer F. Dos Santos Bendict K. Tshoombe Lucas H. B. Santos Ramon C. F. Araujo Allan R. A. Manito Wellington S. Fonseca Marcelo O. Silva Digital Twin-Based Monitoring System of Induction Motors Using IoT Sensors and Thermo-Magnetic Finite Element Analysis IEEE Access Condition monitoring digital twin finite element analysis induction motors Internet of Things |
| title | Digital Twin-Based Monitoring System of Induction Motors Using IoT Sensors and Thermo-Magnetic Finite Element Analysis |
| title_full | Digital Twin-Based Monitoring System of Induction Motors Using IoT Sensors and Thermo-Magnetic Finite Element Analysis |
| title_fullStr | Digital Twin-Based Monitoring System of Induction Motors Using IoT Sensors and Thermo-Magnetic Finite Element Analysis |
| title_full_unstemmed | Digital Twin-Based Monitoring System of Induction Motors Using IoT Sensors and Thermo-Magnetic Finite Element Analysis |
| title_short | Digital Twin-Based Monitoring System of Induction Motors Using IoT Sensors and Thermo-Magnetic Finite Element Analysis |
| title_sort | digital twin based monitoring system of induction motors using iot sensors and thermo magnetic finite element analysis |
| topic | Condition monitoring digital twin finite element analysis induction motors Internet of Things |
| url | https://ieeexplore.ieee.org/document/9998505/ |
| work_keys_str_mv | AT jhenniferfdossantos digitaltwinbasedmonitoringsystemofinductionmotorsusingiotsensorsandthermomagneticfiniteelementanalysis AT bendictktshoombe digitaltwinbasedmonitoringsystemofinductionmotorsusingiotsensorsandthermomagneticfiniteelementanalysis AT lucashbsantos digitaltwinbasedmonitoringsystemofinductionmotorsusingiotsensorsandthermomagneticfiniteelementanalysis AT ramoncfaraujo digitaltwinbasedmonitoringsystemofinductionmotorsusingiotsensorsandthermomagneticfiniteelementanalysis AT allanramanito digitaltwinbasedmonitoringsystemofinductionmotorsusingiotsensorsandthermomagneticfiniteelementanalysis AT wellingtonsfonseca digitaltwinbasedmonitoringsystemofinductionmotorsusingiotsensorsandthermomagneticfiniteelementanalysis AT marceloosilva digitaltwinbasedmonitoringsystemofinductionmotorsusingiotsensorsandthermomagneticfiniteelementanalysis |