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基于有限元法的变参数缺陷牵引电机旋转轴电磁特性研究。

Research on electromagnetic characteristics of traction motor rotary shafts with variable parameter defects based on finite element method.

机构信息

College of Information and Mechanical & Electrical Engineering, Ningde Normal University, Ningde, China.

College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, China.

出版信息

Sci Rep. 2023 Jun 21;13(1):10087. doi: 10.1038/s41598-023-36455-z.

DOI:10.1038/s41598-023-36455-z
PMID:37344486
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10284799/
Abstract

In view of the breakage of the rotary shaft of the traction motor of the locomotive, in order to ensure the reliability and safety of the equipment, this paper studies the magnetic field containing rich information in the space around the rotary shaft, so that the crack defect can be detected and the crack size can be judged in time. In order to study the influence of defect geometric parameters (width, depth and hidden depth) on the spatial magnetic field distribution around the defect in the process of eddy current testing of metal shafts, based on the principle of eddy current testing, a metal rotary shaft detection model with different defect parameters was established by COMSOL software for simulation. The horizontal magnetic induction intensity, vertical magnetic induction intensity and their respective phases are used to analyze the magnetic field distribution around the defect under different parameters. The results show that the magnetic field related parameters can qualitatively and quantitatively evaluate the crack width, qualitatively evaluate the relative size of the crack depth, and qualitatively judge whether there are hidden cracks.

摘要

针对机车牵引电机旋转轴断裂问题，为保证设备的可靠性和安全性，本文研究了旋转轴周围空间中含有丰富信息的磁场，以便及时检测裂纹缺陷并判断裂纹尺寸。为了研究金属轴的涡流检测过程中缺陷几何参数（宽度、深度和埋藏深度）对缺陷周围空间磁场分布的影响，基于涡流检测原理，利用 COMSOL 软件建立了不同缺陷参数的金属旋转轴检测模型进行仿真。采用水平磁感应强度、垂直磁感应强度及其各自相位来分析不同参数下缺陷周围的磁场分布。结果表明，磁场相关参数可以定性和定量评估裂纹宽度，定性评估裂纹深度的相对大小，并定性判断是否存在隐藏裂纹。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cce/10284799/4653e71cd79c/41598_2023_36455_Fig14_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cce/10284799/4653e71cd79c/41598_2023_36455_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cce/10284799/1e630e9a0895/41598_2023_36455_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cce/10284799/45678c561b11/41598_2023_36455_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cce/10284799/750112c578ae/41598_2023_36455_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cce/10284799/8a93caeee913/41598_2023_36455_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cce/10284799/132809dbaa06/41598_2023_36455_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cce/10284799/d72c718cbc5a/41598_2023_36455_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cce/10284799/ea94cd424ac0/41598_2023_36455_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cce/10284799/f4da3ce96010/41598_2023_36455_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cce/10284799/9c29332c53a8/41598_2023_36455_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cce/10284799/f8cace0f3e10/41598_2023_36455_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cce/10284799/d163194eb698/41598_2023_36455_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cce/10284799/4653e71cd79c/41598_2023_36455_Fig14_HTML.jpg

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