Eccentric Reluctance and Permanent Magnet Synchronous Machines Comparison

Mahmoud, Hanafy; Al-Ani, Mahir; Gerada, Chris

doi:10.1109/tia.2018.2848278

Cited by 5 publications

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, due to the daily use of the motor, the abrasion of the rotor also causes the eccentricity [12], which needs to be paid more attention especially in some critical application, such as aerial craft, precise instrument, and high-tech industry [13]. Therefore, it is foremost important to monitor the eccentric error of the motor not only in the manufacturing process, but also in the general usage.…”

Section: Introductionmentioning

confidence: 99%

Artificial Monitoring of Eccentric Synchronous Reluctance Motors Using Neural Networks

Shu-guang¹,

Li²,

Zhao³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

This paper proposes an artificial neural network for monitoring and detecting the eccentric error of synchronous reluctance motors. Firstly, a 15 kW synchronous reluctance motor is introduced and took as a case study to investigate the effects of eccentric rotor. Then, the equivalent magnetic circuits of the studied motor are analyzed and developed, in cases of dynamic eccentric rotor and static eccentric rotor condition, respectively. After that, the analytical equations of the studied motor are derived, in terms of its air-gap flux density, electromagnetic torque, and electromagnetic force, followed by the electromagnetic finite element analyses. Then, the modal analyses of the stator and the whole motor are performed, respectively, to explore the natural frequency and the modal shape of the motor, by which the further vibrational analysis is possible to be conducted. The vibration level of the housing is furtherly studied to investigate its relationship with the rotor eccentricity, which is validated by the prototype test. Furthermore, an artificial neural network, which has 3 layers, is proposed. By taking the air-gap flux density, the electromagnetic force, and the vibrational level as inputs, and taking the eccentric distance as output, the proposed neural network is trained till the error smaller than 5%. Therefore, this neural network is obtaining the input parameters of the tested motor, based on which it is automatically monitoring and reporting the eccentric error to the upper-level control center.

show abstract

Section: Introductionmentioning

confidence: 99%