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1.大连海事大学 轮机工程学院, 辽宁 大连 116026
2.国防科技大学 装备保障技术重点实验室, 湖南 长沙 410073
Received:29 April 2019,
Accepted:24 May 2019,
Published:15 September 2019
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Chen-zhao BAI, Hong-peng ZHANG, Lin ZENG, et al. Inductive oil detecting sensor based on magnetic nanomaterial[J]. Optics and precision engineering, 2019, 27(9): 1960-1967.
Chen-zhao BAI, Hong-peng ZHANG, Lin ZENG, et al. Inductive oil detecting sensor based on magnetic nanomaterial[J]. Optics and precision engineering, 2019, 27(9): 1960-1967. DOI: 10.3788/OPE.20192709.1960.
为了增加电感式油液污染物检测传感器的稳定性,提升对铁磁性和非铁磁性污染物的检测精度,设计了一种内置磁性纳米材料的电感式油液污染物检测传感器,螺线管线圈内部填充的磁性纳米粒子层可以提升检测区域磁场强度,增强磁化涡流效应。模型材料制作300
μ
m的微通道穿过螺线管线圈和磁性纳米材料组成的传感单元,当污染物通过传感单元时,利用电感检测原理可以区分铁磁性和非铁磁性污染物。同时采用有无磁性纳米粒子层的两种传感器进行多组对比实验。实验结果表明,磁性纳米粒材料的电感式油液检测传感器具有更高的检测信噪比以及更低的检测下限,对于20~70
μ
m的铁磁性颗粒检测信噪比提升了20%~25%,对于80~130
μ
m的非铁磁性颗粒的检测信噪比提升了16%~20%。该方法基于微流控检测技术,具有体积小、检测信噪比高等优点,同时为液压油污染物快速检测提供了技术支持,对液压系统的故障诊断与寿命预测具有重要意义。
In order to increase the stability of the inductive oil contaminant detection sensor and improve the detection accuracy of ferromagnetic and nonferromagnetic contaminants
an inductive oil contaminant detection sensor with built-in magnetic nanoparticles was designed in this paper. The solenoid coil was filled with a nanoparticle layer which adsorbs the pollutant particles and enhanced the magnetic field strength of the detection area
enhancing the magnetization and eddy current effects. A 300
μ
m microchannel was made using the model material
which passes through the sensing unit. Ferromagnetic and non-ferromagnetic contaminants can be distinguished when contaminants pass through the microchannel through the sensing unit. At the same time
two sets of contrast experiments were carried out using two sensors with and without magnetic nanoparticle layers. The experimental results show that the inductive oil detection sensor of magnetic nanomaterial has higher detection accuracy and lower detection limit. The detection accuracy of ferromagnetic particles of 20-70
μ
m improved by 20%-25%
while that of nonferromagnetic particles of 80-130
μ
m improved by 16%-25%. The method is based on microfluidic detection technology
and has the advantages of small volume and high detection precision. At the same time
it provides technical support for the rapid detection of hydraulic oil contaminants
which has great significance for fault diagnosis and life prediction of hydraulic systems.
牛云波, 陈桂明.在线磨粒监测传感技术的研究现状与发展趋势[J].传感器世界, 2008, 14(9): 6-9, 37.
NIU Y B, CHEN G M. Studying status and developing trend of on-line wear particle monitoring sensor technology[J]. Sensor World , 2008, 14(9): 6-9, 37. (in Chinese)
吴世友, 王淑芳.液压油污染的来源、危害及其控制措施[J].装备制造技术, 2008(3): 80-82.
WU SH Y, WANG SH F. The sources, harms of hydraulic oil pollution and their controlled measures[J]. Equipment Manufacturing Technology , 2008(3): 80-82. (in Chinese)
ZHANG H P, HUANG W, ZHANG Y D, et al . Design of the microfluidic chip of oil detection[J]. Applied Mechanics and Materials , 2011, 117/118/119: 517-520.
ZHANG R C, YU X, HU Y L, et al . Active control of hydraulic oil contamination to extend the service life of aviation hydraulic system[J]. The International Journal of Advanced Manufacturing Technology , 2018, 96(5/6/7/8): 1693-1704.
周涛, 刘亚龙, 郭静英, 等.液压油污染实验与在线监测模型[J].液压与气动, 2017(11): 72-75.
ZHOU T, LIU Y L, GUO J Y, et al . Experiments of hydraulic oil contamination and on-line monitoring models[J]. Chinese Hydraulics & Pneumatics , 2017(11): 72-75. (in Chinese)
张勇, 司二伟, 李国盛, 等.润滑油金属磨粒传感器设计及试验研究[J].润滑与密封, 2017, 42(4): 89-94.
ZHANG Y, SI E W, LI G SH, et al . Design and experimental study of metal particle sensors for lubricating oils[J]. Lubrication Engineering , 2017, 42(4): 89-94. (in Chinese)
TUCKER J E, GALIE T R, SCHULTZ A, et al . LASERNET fines optical wear debris monitor: a Navy shipboard evaluation of CBM enabling technology[J]. 54th Mach. Fail. Prev. Technol. Proc. , 2000, 191:445-452.
徐超, 张培林, 任国全, 等.新型超声磨粒传感器输出特性研究[J].摩擦学学报, 2015, 35(1): 90-95.
XU CH, ZHANG P L, REN G Q, et al . Output characteristic of a novel online ultrasonic wear debris sensor[J]. Tribology , 2015, 35(1): 90-95. (in Chinese)
ZAREPOUR H, YEO S H. Single abrasive particle impingements as a benchmark to determine material removal modes in micro ultrasonic machining[J]. Wear , 2012, 288: 1-8.
HADI M. Influence of size of abrasive particles in conveyor liquid on ultrasonic cavitation machining process[J]. Applied Mechanics and Materials , 2011, 87: 155-158.
ZHANG H P, CHON C H, PAN X X, et al . Methods for counting particles in microfluidic applications[J]. Microfluidics and Nanofluidics , 2009, 7(6): 739-749.
李绍成, 左洪福, 张艳彬.油液在线监测系统中的磨粒识别[J].光学 精密工程, 2009, 17(3): 589-595.
LI SH CH, ZUO H F, ZHANG Y B. Wear debris recognition for oil on-line monitoring system[J]. Opt. Precision Eng. , 2009, 17(3): 589-595. (in Chinese)
MURALI S, XIA X G, JAGTIANI A V, et al . Capacitive Coulter counting: Detection of metal wear particles in lubricant using a microfluidic device[J] . Smart Materials and Structures , 2009, 18(3): 037001.
张洪朋, 白晨朝, 孙广涛, 等.高通量微型多参数油液污染物检测传感器[J].光学 精密工程, 2018, 26(9): 2237-2245.
ZHANG H P, BAI CH ZH, SUN G T, et al . High-throughput miniature multi-parameter oil contamination detection sensor[J]. Opt. Precision Eng. , 2018, 26(9): 2237-2245. (in Chinese)
DU L, ZHU X L, HAN Y, et al . High throughput wear debris detection in lubricants using a resonance frequency division multiplexed sensor[J]. Tribology Letters , 2013, 51(3): 453-460.
曾霖, 张洪朋, 赵旭鹏, 等.液压油污染物双线圈多参数阻抗检测传感器[J].仪器仪表学报, 2017, 38(7): 1690-1697.
ZENG L, ZHANG H P, ZHAO X P, et al . Double coil multi-parameter impedance sensor for hydraulic oil pollutants detection[J]. Chinese Journal of Scientific Instrument , 2017, 38(7): 1690-1697. (in Chinese)
ZHANG X, ZHANG H, SUN Y, et al . Research on the output characteristics of microfluidic inductive sensor[J]. Journal of Nanomaterials , 2014(1): 1-7.
ZHAO B, ZHANG X M, ZHANG H P, et al . Iron wear particle content measurements in process liquids using micro channel-inductive method[J]. Key Engineering Materials , 2015, 645: 756-760.
陈玉磊.超顺磁性纳米氧化铁的制备及性能研究[D].绵阳: 西南科技大学, 2016.
CHEN Y L. Preparation and Properties of Superparamagnetic Iron Oxide Nanoparticles [D]. Mianyang: Southwest University of Science and Technology, 2016. (in Chinese)
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