journal article·2024-03-01Open access

An online wear metal particles monitoring warning sensor based on electromagnetic induction in lubricating oil

Hui Tao, Yong Zhong, KangWei Qian, Wei Feng

AIP Advances· Volume 14· Issue 3

Abstract

The online monitoring of metal particles in lubricating oil plays an important role in equipment fault diagnosis technology. This paper designs an online wear monitoring warning sensor based on the electromagnetic induction of metal particles in lubricating oil. First, different from the traditional three-coil sensing and detection method, the sensor consists of two excitation coils and one detection coil. In addition, the parallel capacitance and detection capacitance parameters are obtained by COMSOL’s scanning frequency, and the best detection capacitance is 7.53 nF. Second, according to the obtained optimal detection parallel capacitance value, the amplitude data of the particle signal, as well as the ferromagnetic particles above 60 µm and the non-ferromagnetic particles above 80 µm, are obtained. Third, according to the principle of three-coil induction, a mapping relationship model was established between the size range of metal wear particles in lubricating oil. Finally, we set up an oil wear particle platform to verify the sensor performance. The ferromagnetic and non-ferromagnetic particles are divided into multiple size ranges, and the peak-to-peak values of the output signal of different-sized wear particles are measured. We developed a prototype sensor and verified the signal consistency and repeatability of the sensor to wear metal particles. In addition, we used the sensor to test a large number of metal particles of different sizes (14 ferromagnetic particle standard samples with particle sizes ranging from 20.64 to 457.59 µm and ten non-ferromagnetic particle standard samples with particle sizes ranging from 40.52 to 348.07 µm). The experimental results show that the sensor can directly achieve monitoring sensitivity for ferromagnetic particles greater than 64.57 µm and non-ferromagnetic (copper) particles greater than 82.83 µm under the 4.2 mm aperture flow channel. The sensor can effectively realize the sensitivity on monitoring of small ferromagnetic particles and non-ferromagnetic particles, and it is particularly sensitive to small non-ferromagnetic particles.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Lubricants and Their Additives · Non-Destructive Testing Techniques · Corrosion Behavior and Inhibition

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

South China University of Technology 1 , Guangzhou 510640, China

Guangzhou Mechanical Engineering Research Institute Co., Ltd. 2 , Guangzhou 510535, China

References (23)

Show source-provided references
1. Lubricating oil conditioning sensors for online machine health monitoring—A review10.1016/j.triboint.2017.01.015 ↗
2. A critical review of on-line oil wear debris particle detection sensors10.3390/jmse11122363 ↗
3. Design and research of inductive oil pollutant detection sensor based on high gradient magnetic field structure10.3390/mi12060638 ↗
4. Analysis of the effect of velocity on the eddy current effect of metal particles of different materials in inductive bridges10.3390/s22093406 ↗
5. An inductive debris sensor for a large-diameter lubricating oil circuit based on a high-gradient magnetic field10.3390/app9081546 ↗
6. A highly sensitive triple-coil inductive debris sensor based on an effective unbalance compensation circuit10.1088/1361-6501/aaf119 ↗
7. High throughput wear debris detection in lubricants using a resonance frequency division multiplexed sensor10.1007/s11249-013-0179-x ↗
8. A novel on-chip impedance sensor for the detection of particle contamination in hydraulic oil10.3390/mi8080249 ↗
9. An impedance debris sensor based on a high-gradient magnetic field for high sensitivity and high throughput10.1109/tie.2020.2988237 ↗
10. Research on machine wear process using time series modeling method10.13433/j.cnki.1003-8728.2000.02.032 ↗
11. A predictive model and experimental study on diesel engine wear10.16078/j.tribology.1996.04.010 ↗
12. Prediction of elevation error of cantilever construction main beam based on grey residual metabolism model10.3963/j.issn.1006-2823.2009.04.005 ↗
13. The influence of the radial distribution of wear particles on the test results of inductive wear particle sensors10.3969/j.issn.1004-1699.2010.07.014 ↗
14. A microfluidic Coulter counting device for metal wear detection in lubrication oil10.1063/1.3072665 ↗
15. Characteristics of ferromagnetic wear particles in inductive wear particle sensors simulation research10.19650/j.cnki.cjsi.2011.12.021 ↗
16. Design of inductive oil and wear particle sensor system10.3901/JME.2021.12.126 ↗
17. Inductive wear particle sensor induced electromotive force extraction method10.16450/j.cnki.issn.1004-6801.2016.01.006 ↗
18. In-line oil debris monitor for aircraft engine condition assessment
19. The influence of wear particles on the wear status of gearbox10.16577/j.cnki.42-1215/tb.2018.06.009 ↗
20. Research progress on fault diagnosis and early warning methods for fan gearbox10.16577/j.cnki.CNKI:SUN:YQYB.0.2018-01-001 ↗
21. Research on the influence of inductive wear particle sensor coils on debris detection10.1063/5.0090506 ↗
22. A 3 × 3 wear debris sensor array for real time lubricant oil conditioning monitoring using synchronized sampling10.1016/j.ymssp.2016.06.014 ↗
23. Research on high sensitivity oil debris detection sensor using high magnetic permeability material and coil mutual inductance10.3390/s22051833 ↗