1. College of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan, Shanxi 030024, China
2. Shanxi Provincial Key Laboratory of Coal Mine Fully Mechanized Mining Equipment, TUT,
Taiyuan, Shanxi 030024, China
3. Key Laboratory of Modern Design and Rotor Bearing System, Ministry of Education, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China
4. Shanxi Keda Automatic Control Co., Ltd., Taiyuan, Shanxi 030006, China
5. College of Aeronautics and Astronautics, TUT, Jinzhong, Shanxi 030600, China
Abstract：Three-coil electromagnetic abrasive sensor is widely used in oil online monitoring, and the bubbles in the oil are easy to interfere with the detection of copper particles, and the quantitative analysis of the influence of bubbles in particle detection will help to improve the detection accuracy of electromagnetic abrasive sensors. Based on the magnetic coupling effect in the detection of electromagnetic abrasive particles, the influence characteristics of bubble gap, bubble size, bubble position and number of bubbles on the detection of copper particles were studied, and the electromagnetic abrasive sensor with large diameter channel (38mm) was simulated and tested. The results show that the larger the gap between the bubble and the copper particles, the smaller the signal amplitude of the copper particles, and the influence range is (0.52~10.31)%. The signal amplitude of copper particles also increased when the bubble size increased, and the influence of bubble diameter was 3.07% and 5.47% when the bubble diameter was 20mm and 25mm, respectively. The larger the angle between the center line of the bubble and the copper particle and the center axis of the sensor, the smaller the detection impact, and the influence range is (0.4~5.6)%; The greater the number of bubbles, the greater the disturbance, and the influence degree of single and double bubbles was 5.66% and 8.74%, respectively.
QIU L J, XUAN Z N, ZHANG X F. Research progress in oil online ［J］. Monitoring Technology Transducer and Microsystems, 2015, 34(4): 4-7.
SUN Y S, YANG H, TONG H B, et al. On-line monitoring of aero-engine lubricating oil abrasive grains［J］. Chinese Journal of Scientific Instrument, 2017, 38(7): 1561-1569.
JIA R, MA B. Magnetic properties of ferromagnetic particles under alternating magnetic fields: Focus on particle detection sensor applications［J］. Sensors, 2018(18): 1-17.
LIU SH M, LEI B, LI ZH Y, et al. Design and experimental study of coil drive drive coil ［J］. Foreign Electronic Measurement Technology, 2011, 30(8): 54-57.
JIA R, MA B, ZHENG Ch S, et al. A method for improving the sensitivity of inductive abrasive grain online monitoring sensor［J］. Journal of Hunan University (Natural Science), 2018, 45(4): 129-137.
ZHAO B, ZHAO M, MA Y M, et al. Research on blind source separation of double/single-channel conducted electromagnetic interference noise［J］. Atca Metrologica Sinica, 2016, 37(5): 535-539.
ZHANG X Z, LI Y T, HE T. Three-dimensional characteristics of virtual current of electromagnetic flowmeter containing a bubble［J］. Acta Metrologica Sinica, 2002, 23(3): 195-198.
WANG L Y, ZHONG H, LI L, et al. Effect of coil spacing on output signal of oil abrasive detection sensor［J］. Lubrication and Sealing, 2020, 45(6): 69-75.
［14］
张猛. 检测油液中金属磨粒的磁芯式电涡流传感器研究［D］. 天津: 天津工业大学, 2020.
BA X, WANG J F, ZUO Y B, et al. On-line oil monitoring system based on FPGA［J］. Instrumentation Technology and Sensors, 2015 (6): 103-106.
FENG F, WANG G D, SHU M H. Design optimization of resonant coil for low power radio energy transmission system［J］. Electronic Measurement Technology, 2017, 40(1): 12-16.
WANG Z L, FU X, LIANG C J, Sensing signal detection method of oil metal chip based on continuous wavelet transform and curve fitting［J］. Journal of Electronic Measurement and Instrumentation, 2021,35(11):32-38.
2024, Vol. 45 
