Streamlined Bearing Fault Detection Using Artificial Intelligence in Permanent Magnet Synchronous Motors
Permanent magnet synchronous motors (PMSMs) are widely used in industrial applications due to their high efficiency and reliability. However, bearing faults remain a critical issue, necessitating robust fault detection strategies. This paper proposes an edge–fog–cloud architecture for bearing fault...
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MDPI AG
2025-04-01
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| Online Access: | https://www.mdpi.com/2075-1702/13/5/357 |
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| author | Javier de las Morenas Lidia M. Belmonte Rafael Morales |
| author_facet | Javier de las Morenas Lidia M. Belmonte Rafael Morales |
| author_sort | Javier de las Morenas |
| collection | DOAJ |
| description | Permanent magnet synchronous motors (PMSMs) are widely used in industrial applications due to their high efficiency and reliability. However, bearing faults remain a critical issue, necessitating robust fault detection strategies. This paper proposes an edge–fog–cloud architecture for bearing fault detection with a specific focus on implementing an efficient and non-intrusive edge-based solution. The methodology involves preprocessing motor current signals through fast Fourier transform (FFT) and Hilbert transform-based envelope analysis to extract harmonics without being masked by the fundamental supply frequency. These features are used to train machine learning models, considering variations in both speed and load. Experimental validation is conducted using the Paderborn University Bearing Dataset, demonstrating that the proposed approach achieves exceptional accuracy, precision, recall, and F1-score, exceeding 0.98 with models such as XGBoost, LightGBM, and CatBoost. While CatBoost exhibits the highest performance, LightGBM is selected as the optimal model due to its significantly reduced training time, making it well suited for edge computing applications. A comparison with prior studies confirms that the proposed method delivers competitive performance while utilizing fewer sensors, reducing hardware complexity. This research lays the groundwork for future predictive maintenance strategies ensuring real-time diagnostics and optimized industrial deployment. |
| format | Article |
| id | doaj-art-6166f897a6174cf782f5b66df36ca4a4 |
| institution | OA Journals |
| issn | 2075-1702 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Machines |
| spelling | doaj-art-6166f897a6174cf782f5b66df36ca4a42025-08-20T02:33:50ZengMDPI AGMachines2075-17022025-04-0113535710.3390/machines13050357Streamlined Bearing Fault Detection Using Artificial Intelligence in Permanent Magnet Synchronous MotorsJavier de las Morenas0Lidia M. Belmonte1Rafael Morales2Escuela Técnica Superior de Ingeniería Industrial de Albacete, Universidad de Castilla-La Mancha, 02071 Albacete, SpainEscuela Técnica Superior de Ingeniería Industrial de Albacete, Universidad de Castilla-La Mancha, 02071 Albacete, SpainInstituto de Investigación en Informática (I3A), Universidad de Castilla-La Mancha, 02071 Albacete, SpainPermanent magnet synchronous motors (PMSMs) are widely used in industrial applications due to their high efficiency and reliability. However, bearing faults remain a critical issue, necessitating robust fault detection strategies. This paper proposes an edge–fog–cloud architecture for bearing fault detection with a specific focus on implementing an efficient and non-intrusive edge-based solution. The methodology involves preprocessing motor current signals through fast Fourier transform (FFT) and Hilbert transform-based envelope analysis to extract harmonics without being masked by the fundamental supply frequency. These features are used to train machine learning models, considering variations in both speed and load. Experimental validation is conducted using the Paderborn University Bearing Dataset, demonstrating that the proposed approach achieves exceptional accuracy, precision, recall, and F1-score, exceeding 0.98 with models such as XGBoost, LightGBM, and CatBoost. While CatBoost exhibits the highest performance, LightGBM is selected as the optimal model due to its significantly reduced training time, making it well suited for edge computing applications. A comparison with prior studies confirms that the proposed method delivers competitive performance while utilizing fewer sensors, reducing hardware complexity. This research lays the groundwork for future predictive maintenance strategies ensuring real-time diagnostics and optimized industrial deployment.https://www.mdpi.com/2075-1702/13/5/357non-intrusive condition monitoringbearing fault detectionedge–fog–cloud computingartificial intelligenceraw data preprocessing |
| spellingShingle | Javier de las Morenas Lidia M. Belmonte Rafael Morales Streamlined Bearing Fault Detection Using Artificial Intelligence in Permanent Magnet Synchronous Motors Machines non-intrusive condition monitoring bearing fault detection edge–fog–cloud computing artificial intelligence raw data preprocessing |
| title | Streamlined Bearing Fault Detection Using Artificial Intelligence in Permanent Magnet Synchronous Motors |
| title_full | Streamlined Bearing Fault Detection Using Artificial Intelligence in Permanent Magnet Synchronous Motors |
| title_fullStr | Streamlined Bearing Fault Detection Using Artificial Intelligence in Permanent Magnet Synchronous Motors |
| title_full_unstemmed | Streamlined Bearing Fault Detection Using Artificial Intelligence in Permanent Magnet Synchronous Motors |
| title_short | Streamlined Bearing Fault Detection Using Artificial Intelligence in Permanent Magnet Synchronous Motors |
| title_sort | streamlined bearing fault detection using artificial intelligence in permanent magnet synchronous motors |
| topic | non-intrusive condition monitoring bearing fault detection edge–fog–cloud computing artificial intelligence raw data preprocessing |
| url | https://www.mdpi.com/2075-1702/13/5/357 |
| work_keys_str_mv | AT javierdelasmorenas streamlinedbearingfaultdetectionusingartificialintelligenceinpermanentmagnetsynchronousmotors AT lidiambelmonte streamlinedbearingfaultdetectionusingartificialintelligenceinpermanentmagnetsynchronousmotors AT rafaelmorales streamlinedbearingfaultdetectionusingartificialintelligenceinpermanentmagnetsynchronousmotors |