Development of a Fault-Tolerant Permanent Magnet Synchronous Motor Using a Machine-Learning Algorithm for a Predictive Maintenance Elevator
Elevators serve as essential vertical transportation systems for both passengers and heavy loads in modern buildings. Electromechanical lifts have become the dominant choice due to their performance advantages over hydraulic systems. A critical component of their drive mechanism is the Permanent Mag...
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MDPI AG
2025-05-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/13/5/427 |
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| author | Vasileios I. Vlachou Theoklitos S. Karakatsanis |
| author_facet | Vasileios I. Vlachou Theoklitos S. Karakatsanis |
| author_sort | Vasileios I. Vlachou |
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| description | Elevators serve as essential vertical transportation systems for both passengers and heavy loads in modern buildings. Electromechanical lifts have become the dominant choice due to their performance advantages over hydraulic systems. A critical component of their drive mechanism is the Permanent Magnet Synchronous Motor (PMSM), which is subject to mechanical and electrical stress during continuous operation. This necessitates advanced monitoring techniques to ensure safety, system reliability, and reduced maintenance costs. In this study, a fault-tolerant PMSM is designed and evaluated through 2D Finite Element Analysis (FEA), optimizing key electromagnetic parameters. The design is validated through experimental testing on a real elevator setup, capturing operational data under various loading conditions. These signals are preprocessed and analyzed using advanced machine-learning techniques, specifically a Random Forest classifier, to distinguish between Normal, Marginal, and Critical states of motor health. The model achieved a classification accuracy of 94%, demonstrating high precision in predictive maintenance capabilities. The results confirm that integrating a fault-tolerant PMSM design with real-time data analytics offers a reliable solution for early fault detection, minimizing downtime and enhancing elevator safety. |
| format | Article |
| id | doaj-art-8cd5c68b5d9844e5a6773b8d89491c8e |
| institution | Kabale University |
| issn | 2075-1702 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-8cd5c68b5d9844e5a6773b8d89491c8e2025-08-20T03:47:54ZengMDPI AGMachines2075-17022025-05-0113542710.3390/machines13050427Development of a Fault-Tolerant Permanent Magnet Synchronous Motor Using a Machine-Learning Algorithm for a Predictive Maintenance ElevatorVasileios I. Vlachou0Theoklitos S. Karakatsanis1School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, GreeceDepartment of Production and Management Engineering, Democritus University of Thrace, 67100 Xanthi, GreeceElevators serve as essential vertical transportation systems for both passengers and heavy loads in modern buildings. Electromechanical lifts have become the dominant choice due to their performance advantages over hydraulic systems. A critical component of their drive mechanism is the Permanent Magnet Synchronous Motor (PMSM), which is subject to mechanical and electrical stress during continuous operation. This necessitates advanced monitoring techniques to ensure safety, system reliability, and reduced maintenance costs. In this study, a fault-tolerant PMSM is designed and evaluated through 2D Finite Element Analysis (FEA), optimizing key electromagnetic parameters. The design is validated through experimental testing on a real elevator setup, capturing operational data under various loading conditions. These signals are preprocessed and analyzed using advanced machine-learning techniques, specifically a Random Forest classifier, to distinguish between Normal, Marginal, and Critical states of motor health. The model achieved a classification accuracy of 94%, demonstrating high precision in predictive maintenance capabilities. The results confirm that integrating a fault-tolerant PMSM design with real-time data analytics offers a reliable solution for early fault detection, minimizing downtime and enhancing elevator safety.https://www.mdpi.com/2075-1702/13/5/427permanent magnet machinefault diagnosiscondition monitoringshort circuitfault tolerancepredictive maintenance |
| spellingShingle | Vasileios I. Vlachou Theoklitos S. Karakatsanis Development of a Fault-Tolerant Permanent Magnet Synchronous Motor Using a Machine-Learning Algorithm for a Predictive Maintenance Elevator Machines permanent magnet machine fault diagnosis condition monitoring short circuit fault tolerance predictive maintenance |
| title | Development of a Fault-Tolerant Permanent Magnet Synchronous Motor Using a Machine-Learning Algorithm for a Predictive Maintenance Elevator |
| title_full | Development of a Fault-Tolerant Permanent Magnet Synchronous Motor Using a Machine-Learning Algorithm for a Predictive Maintenance Elevator |
| title_fullStr | Development of a Fault-Tolerant Permanent Magnet Synchronous Motor Using a Machine-Learning Algorithm for a Predictive Maintenance Elevator |
| title_full_unstemmed | Development of a Fault-Tolerant Permanent Magnet Synchronous Motor Using a Machine-Learning Algorithm for a Predictive Maintenance Elevator |
| title_short | Development of a Fault-Tolerant Permanent Magnet Synchronous Motor Using a Machine-Learning Algorithm for a Predictive Maintenance Elevator |
| title_sort | development of a fault tolerant permanent magnet synchronous motor using a machine learning algorithm for a predictive maintenance elevator |
| topic | permanent magnet machine fault diagnosis condition monitoring short circuit fault tolerance predictive maintenance |
| url | https://www.mdpi.com/2075-1702/13/5/427 |
| work_keys_str_mv | AT vasileiosivlachou developmentofafaulttolerantpermanentmagnetsynchronousmotorusingamachinelearningalgorithmforapredictivemaintenanceelevator AT theoklitosskarakatsanis developmentofafaulttolerantpermanentmagnetsynchronousmotorusingamachinelearningalgorithmforapredictivemaintenanceelevator |