Estimation Strategy of RUL Calculation in the Case of Crack in the Magnets of PMM Used in HEV Application
Knowing the importance of assuring their reliability and availability, prognosis and remaining useful life calculation (RUL) concepts are highly suggested to be applied in critical applications such as hybrid electric vehicles (HEV). In the electrical propulsion system of HEVs, the electrical machin...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/6/3694 |
| _version_ | 1797613613233471488 |
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| author | Riham Ginzarly Ghaleb Hoblos Nazih Moubayed |
| author_facet | Riham Ginzarly Ghaleb Hoblos Nazih Moubayed |
| author_sort | Riham Ginzarly |
| collection | DOAJ |
| description | Knowing the importance of assuring their reliability and availability, prognosis and remaining useful life calculation (RUL) concepts are highly suggested to be applied in critical applications such as hybrid electric vehicles (HEV). In the electrical propulsion system of HEVs, the electrical machine is one of the most critical elements considering its cost and function. Most electrical machines used in HEVs are permanent magnet machines (PMM). Most severe faults in PMM that affect its normal operation are the result of demagnetization. However, applying prognosis to a real prototype to detect the presence of mechanical defects such as cracks in the magnet of PMM and calculating the RUL of this defective element are challenging. In this paper, we are going to take advantage of a finite element model already built for the PMM in the healthy state and the state where cracks of different depths are integrated into the magnet. After that, relevant vital parameters that are affected when this type of fault persists in the machine are collected. Then, prognosis is applied to detect the presence of the crack in one piece of magnet in the electrical machine. Following this, the RUL calculation is performed to predict the remaining time before the crack propagates and a total fracture occurs in the magnet. The method used to execute the prognosis is the hidden Markov model (HMM). The RUL calculation will be performed using Paris equation, being the most important equation that models the growth and propagation of cracks |
| first_indexed | 2024-03-11T06:58:18Z |
| format | Article |
| id | doaj.art-272346538d1f4a348f4f67efbc900021 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T06:58:18Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-272346538d1f4a348f4f67efbc9000212023-11-17T09:25:26ZengMDPI AGApplied Sciences2076-34172023-03-01136369410.3390/app13063694Estimation Strategy of RUL Calculation in the Case of Crack in the Magnets of PMM Used in HEV ApplicationRiham Ginzarly0Ghaleb Hoblos1Nazih Moubayed2College of Engineering and Technology, American University of the Middle East, Egaila 54200, KuwaitNormandy University, UNIROUEN, ESIGELEC, IRSEEM, 76000 Rouen, FranceLaRGES, Faculty of Engineering 1, Lebanese University, Tripoli 1300, LebanonKnowing the importance of assuring their reliability and availability, prognosis and remaining useful life calculation (RUL) concepts are highly suggested to be applied in critical applications such as hybrid electric vehicles (HEV). In the electrical propulsion system of HEVs, the electrical machine is one of the most critical elements considering its cost and function. Most electrical machines used in HEVs are permanent magnet machines (PMM). Most severe faults in PMM that affect its normal operation are the result of demagnetization. However, applying prognosis to a real prototype to detect the presence of mechanical defects such as cracks in the magnet of PMM and calculating the RUL of this defective element are challenging. In this paper, we are going to take advantage of a finite element model already built for the PMM in the healthy state and the state where cracks of different depths are integrated into the magnet. After that, relevant vital parameters that are affected when this type of fault persists in the machine are collected. Then, prognosis is applied to detect the presence of the crack in one piece of magnet in the electrical machine. Following this, the RUL calculation is performed to predict the remaining time before the crack propagates and a total fracture occurs in the magnet. The method used to execute the prognosis is the hidden Markov model (HMM). The RUL calculation will be performed using Paris equation, being the most important equation that models the growth and propagation of crackshttps://www.mdpi.com/2076-3417/13/6/3694hybrid electric vehiclepermanent magnet machinedemagnetizationremaining useful life calculationParis equationhidden Markova model |
| spellingShingle | Riham Ginzarly Ghaleb Hoblos Nazih Moubayed Estimation Strategy of RUL Calculation in the Case of Crack in the Magnets of PMM Used in HEV Application Applied Sciences hybrid electric vehicle permanent magnet machine demagnetization remaining useful life calculation Paris equation hidden Markova model |
| title | Estimation Strategy of RUL Calculation in the Case of Crack in the Magnets of PMM Used in HEV Application |
| title_full | Estimation Strategy of RUL Calculation in the Case of Crack in the Magnets of PMM Used in HEV Application |
| title_fullStr | Estimation Strategy of RUL Calculation in the Case of Crack in the Magnets of PMM Used in HEV Application |
| title_full_unstemmed | Estimation Strategy of RUL Calculation in the Case of Crack in the Magnets of PMM Used in HEV Application |
| title_short | Estimation Strategy of RUL Calculation in the Case of Crack in the Magnets of PMM Used in HEV Application |
| title_sort | estimation strategy of rul calculation in the case of crack in the magnets of pmm used in hev application |
| topic | hybrid electric vehicle permanent magnet machine demagnetization remaining useful life calculation Paris equation hidden Markova model |
| url | https://www.mdpi.com/2076-3417/13/6/3694 |
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