| Title | Demagnetization Modeling Research for Permanent Magnet in PMSLM Using Extreme Learning Machine |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Song, Juncai, Zhao, Jiwen, Dong, Fei, Zhao, Jing, Xu, Liang, Wang, Lijun, Xie, Fang |
| Conference Name | 2019 IEEE International Electric Machines Drives Conference (IEMDC) |
| Keywords | compositionality, Data models, demagnetisation, Demagnetization, ELM, extreme learning machine, extreme learning machine (ELM), finite element analysis, Fitting, learning (artificial intelligence), linear modeling method, linear synchronous motors, machine learning algorithm, machine theory, magnetic distribution, permanent magnet motors, permanent magnet synchronous linear motor, permanent magnet synchronous linear motor (PMSLM), Permanent magnets, permanent magnets (PM), PM characteristics, PMSLM, polynomial modeling method, polynomials, power engineering computing, pubcrawl, remanence, Resiliency, temperature 293.0 K to 298.0 K, temperature 300.0 degC, temperature demagnetization modeling method, Temperature distribution, Temperature measurement |
| Abstract | This paper investigates the temperature demagnetization modeling method for permanent magnets (PM) in permanent magnet synchronous linear motor (PMSLM). First, the PM characteristics are presented, and finite element analysis (FEA) is conducted to show the magnetic distribution under different temperatures. Second, demagnetization degrees and remanence of the five PMs' experiment sample are actually measured in stove at temperatures varying from room temperature to 300 degC, and to obtain the real data for next-step modeling. Third, machine learning algorithm called extreme learning machine (ELM) is introduced to map the nonlinear relationships between temperature and demagnetization characteristics of PM and build the demagnetization models. Finally, comparison experiments between linear modeling method, polynomial modeling method, and ELM can certify the effectiveness and advancement of this proposed method. |
| DOI | 10.1109/IEMDC.2019.8785136 |
| Citation Key | song_demagnetization_2019 |
Demagnetization Modeling Research for Permanent Magnet in PMSLM Using Extreme Learning Machine