Calculation of Flux Leakage Coefficient of PMSM with Interior V-Type Magnets and FSCW

2018 ◽  
Author(s):  
Gu Yuxi ◽  
Gao Peng ◽  
Wang Xiaoyuan
Keyword(s):  
Leakage Coefficient ◽  
Flux Leakage

Finite Element Analysis on Magnetic Flux Leakage of Interior Permanent Magnet Generator for Vehicle

2011 ◽  
Vol 66-68 ◽  
pp. 483-488 ◽  
Author(s):  
Xue Yi Zhang ◽  
Hong Bin Yin ◽  
Li Wei Shi
Keyword(s):  
Finite Element ◽  
Magnetic Flux ◽  
Permanent Magnet ◽  
Structural Design ◽  
Magnetic Flux Leakage ◽  
The Finite Element Method ◽  
Element Analysis ◽  
Interior Permanent Magnet ◽  
Leakage Coefficient ◽  
Flux Leakage

In order to solve the problem that no-load magnetic flux leakage coefficient is not accurate when it is calculated by the method of magnetic circuit, a model of interior permanent magnet(IPM) generator with 36 slots for vehicle was built through the finite element method of the ANSYS, and then a means of calculating the IPM generator’s magnetic flux was put forward after analyzing the magnetic flux leakage conditions of different rotor structures under the circumstance of not changing stator structure. The ralationships among the pairs of poles, the magnet width, the thickness of non-magnetic sleeve, the length of air-gap and the magnetic flux leakage coefficient were obtained, and they provided forceful guidance for the structural design of IPM generator.

A Narrow Bridge Structure for Reduction of the Flux Leakage

2018 ◽  
Vol 138 (5) ◽  
pp. 402-409
Author(s):  
Makoto Ito ◽  
Shinji Sugimoto ◽  
Akeshi Takahashi ◽  
Shuichi Tamiya
Keyword(s):  
Bridge Structure ◽  
Narrow Bridge ◽  
Flux Leakage

scholarly journals Location Detection Method of Detector in Pipeline Using VMD Algorithm and Machine Learning Classifier

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1436
Author(s):  
Tuoru Li ◽  
Senxiang Lu ◽  
Enjie Xu
Keyword(s):  
Machine Learning ◽  
Magnetic Flux ◽  
Magnetic Flux Leakage ◽  
Intrinsic Mode Functions ◽  
Detection Range ◽  
Vibration Signals ◽  
Feature Vectors ◽  
Learning Classifier ◽  
Mode Decomposition ◽  
Flux Leakage

The internal detector in a pipeline needs to use the ground marker to record the elapsed time for accurate positioning. Most existing ground markers use the magnetic flux leakage testing principle to detect whether the internal detector passes. However, this paper uses the method of detecting vibration signals to track and locate the internal detector. The Variational Mode Decomposition (VMD) algorithm is used to extract features, which solves the defect of large noise and many disturbances of vibration signals. In this way, the detection range is expanded, and some non-magnetic flux leakage internal detectors can also be located. Firstly, the extracted vibration signals are denoised by the VMD algorithm, then kurtosis value and power value are extracted from the intrinsic mode functions (IMFs) to form feature vectors, and finally the feature vectors are input into random forest and Multilayer Perceptron (MLP) for classification. Experimental research shows that the method designed in this paper, which combines VMD with a machine learning classifier, can effectively use vibration signals to locate the internal detector and has the characteristics of high accuracy and good adaptability.

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