A Resistance-Inductance Debris Sensor Based on Microfluidic Fabrication

被引:0
|
作者
Shi H. [1 ]
Zhang H. [1 ]
Xie Y. [1 ]
Sun Y. [1 ]
机构
[1] College of Marine Engineering, Dalian Maritime University, Liaoning, Dalian
关键词
debris sensor; inductance parameter; microfluidic fabrication; resistance parameter;
D O I
10.3969/j.issn.1004-132X.2022.20.010
中图分类号
学科分类号
摘要
A microfluidic-based sensor which might detect resistance and inductance parameters was fabricated to achieve high-precision measurement of metal debris in oil. The characteristics of magnetization and eddy current effects of metal particles in harmonic magnetic field were obtained by simulation, and the voltage and frequency characteristics of resistance and inductance detections were also studied by experiments. High-frequency excitation might enhance the eddy current effects inside metal particles, and excitation voltages had little effects on the detection results of sensors. The results show that inductance parameter has stronger detection ability for ferromagnetic metals, and resistance parameter has stronger detection ability for non-ferromagnetic metals. At 2.0 V, 2.0 MHz excitation, the sensor may effectively identify 60 jum diameter copper particles and 16 ,um diameter iron particles by comparing and analyzing resistance and inductance detection results. The method of detecting non-ferromagnetic metal debris based on coil resistance parameters provides a new way to enhance the comprehensive performance of debris sensors. © 2022 China Mechanical Engineering Magazine Office. All rights reserved.
引用
收藏
页码:2468 / 2475
页数:7
相关论文
共 21 条
  • [1] ZHANG Yingbo, JIA Yunxian, QIU Guodong, Et al., Stochastic Filtering Residual Useful Life Prediction Model Based on Metal Concentration Gradient in Lubricant [J], Systems Engineering Theory 8 Practice, 34, 6, pp. 1620-1625, (2014)
  • [2] YAN Shufa, MA Biao, ZHENG Changsong, Et al., Wear Localization and Identification under Nonlinear Condition Monitoring Data[J], Journal of Jilin University (Engineering and Technology Edition), 49, 2, pp. 359-365, (2019)
  • [3] YAN Shufa, MA Biao, ZHENG Changsong, Condition-based Maintenance for Power-shift Steering Transmission Based on Oil Spectral Analysis, Spectroscopy and Spectral Analysis, 39, 11, pp. 3470-3474, (2019)
  • [4] XU Bin, WEN Guangrui, SU Yu, Et al., Application of Multi-level Information Fusion for Wear Particle Recognition of Ferrographic Images, Optics and Precision Engineering, 26, 6, pp. 1551-1560, (2018)
  • [5] WU T H, WU H K, DU Y, Et al., Progress and Trend of Sensor Technology for On-line Oil Moni-toring[J], Science China, 56, 12, pp. 2914-2926, (2013)
  • [6] HAO Yanlong, HE Hongkun, CHANG Qing, Et al., On-line Analysis for Particles in Lubricating Oil Based on Micro-image Recognition Method[J], Lubrication Engineering, 41, 5, pp. 59-64, (2016)
  • [7] Lijianyue ZHAO Liping, Yiwei LIANG, Bubble Recognition Method Based on Particle Counter of Light-blocking Theory [J], Instrument Technique and Sensor, 9, pp. 29-32, (2018)
  • [8] Chun LYU, ZHANG Peilin, WU Dinghai, Et al., Research on Online Monitoring System for Oil Wear Debris Based on Ultrasonic Sensor [J], Machine Tool & Hydraulics, 44, 7, pp. 73-75, (2016)
  • [9] YANG Hao, SUN Yanshan, LI Jian, Et al., Variational Mode Decomposition and Probability Density Estimation of Lubricating Oil Debris Detection Signal [J], Chinese Journal of Scientific Instrument, 39, 4, pp. 99-106, (2018)
  • [10] SHI Haotian, ZHANG Hongpeng, WANG Wenqi, Et al., Design and Research of High-sensitivity Wear Debris Detection Sensor[J], Optics and Precision Engineering, 27, 9, pp. 2043-2052, (2019)