Health assessment of a brushless direct current motor stator using a physics-informed long short-term memory network
The brushless direct-current (BLDC) motor is widely regarded as a promising and versatile electronically controlled motor. Ensuring its reliable operation is crucial for maintaining production efficiency and safety. The stator is essential to the functioning of BLDC motors, and the non-invasive heal...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | International Journal of Electrical Power & Energy Systems |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061524006252 |
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| author | Yi Ren Runfei Yi Zhaoxin Lian Quan Xia Dezhen Yang Bo Sun Qiang Feng |
| author_facet | Yi Ren Runfei Yi Zhaoxin Lian Quan Xia Dezhen Yang Bo Sun Qiang Feng |
| author_sort | Yi Ren |
| collection | DOAJ |
| description | The brushless direct-current (BLDC) motor is widely regarded as a promising and versatile electronically controlled motor. Ensuring its reliable operation is crucial for maintaining production efficiency and safety. The stator is essential to the functioning of BLDC motors, and the non-invasive health assessment of the stator can minimise operational disruptions to the motor while ensuring reliability. In this study, we propose a physics-informed long short-term memory network for the non-invasive assessment of BLDC motor stator health conditions. The proposed framework combines physics-informed neural networks (PINNs) with long short-term memory networks by optimising the structure and loss functions of the PINNs to thereby leverage the complementary strengths of neural networks and physical models to enhance their predictive capabilities. A case study involving a BLDC motor system validated the feasibility and effectiveness of the proposed method and yielded precise and reliable results. These results indicate that integrating physical information into deep learning methods is a promising approach by which to assess BLDC motor stator health. |
| format | Article |
| id | doaj-art-b9af07b37e90445d818338a257ae1e03 |
| institution | Kabale University |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-b9af07b37e90445d818338a257ae1e032025-01-19T06:23:55ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-03-01164110402Health assessment of a brushless direct current motor stator using a physics-informed long short-term memory networkYi Ren0Runfei Yi1Zhaoxin Lian2Quan Xia3Dezhen Yang4Bo Sun5Qiang Feng6School of Reliability and Systems Engineering, Beihang University, Beijing, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing, ChinaCorresponding author.; School of Reliability and Systems Engineering, Beihang University, Beijing, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing, ChinaThe brushless direct-current (BLDC) motor is widely regarded as a promising and versatile electronically controlled motor. Ensuring its reliable operation is crucial for maintaining production efficiency and safety. The stator is essential to the functioning of BLDC motors, and the non-invasive health assessment of the stator can minimise operational disruptions to the motor while ensuring reliability. In this study, we propose a physics-informed long short-term memory network for the non-invasive assessment of BLDC motor stator health conditions. The proposed framework combines physics-informed neural networks (PINNs) with long short-term memory networks by optimising the structure and loss functions of the PINNs to thereby leverage the complementary strengths of neural networks and physical models to enhance their predictive capabilities. A case study involving a BLDC motor system validated the feasibility and effectiveness of the proposed method and yielded precise and reliable results. These results indicate that integrating physical information into deep learning methods is a promising approach by which to assess BLDC motor stator health.http://www.sciencedirect.com/science/article/pii/S0142061524006252Physics-informed neural networkBLDC motorHealth assessmentElectromagnetic simulationStator fault |
| spellingShingle | Yi Ren Runfei Yi Zhaoxin Lian Quan Xia Dezhen Yang Bo Sun Qiang Feng Health assessment of a brushless direct current motor stator using a physics-informed long short-term memory network International Journal of Electrical Power & Energy Systems Physics-informed neural network BLDC motor Health assessment Electromagnetic simulation Stator fault |
| title | Health assessment of a brushless direct current motor stator using a physics-informed long short-term memory network |
| title_full | Health assessment of a brushless direct current motor stator using a physics-informed long short-term memory network |
| title_fullStr | Health assessment of a brushless direct current motor stator using a physics-informed long short-term memory network |
| title_full_unstemmed | Health assessment of a brushless direct current motor stator using a physics-informed long short-term memory network |
| title_short | Health assessment of a brushless direct current motor stator using a physics-informed long short-term memory network |
| title_sort | health assessment of a brushless direct current motor stator using a physics informed long short term memory network |
| topic | Physics-informed neural network BLDC motor Health assessment Electromagnetic simulation Stator fault |
| url | http://www.sciencedirect.com/science/article/pii/S0142061524006252 |
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