Bai Chenzhao, Zhang Hongpeng, Zeng Lin, Zhao Xupeng, Ma Laihao
College of Marine Engineering, Dalian Maritime University, Dalian 116026, China.
Micromachines (Basel). 2020 Feb 10;11(2):183. doi: 10.3390/mi11020183.
The wear debris in hydraulic oil or lubricating oil has a wealth of equipment operating information, which is an important basis for large mechanical equipment detection and fault diagnosis. Based on traditional inductive oil detection technology, magnetic nanoparticles are exploited in this paper. A new inductive oil detection sensor is designed based on the characteristics of magnetic nanoparticles. The sensor improves detection sensitivity based on distinguishing between ferromagnetic and non-ferromagnetic wear debris. Magnetic nanoparticles increase the internal magnetic field strength of the solenoid coil and the stability of the internal magnetic field of the solenoid coil. During the experiment, the optimal position of the sensor microchannel was first determined, then the effect of the magnetic nanoparticles on the sensor's detection was confirmed, and finally the concentration ratio of the mixture was determined. The experimental results show that the inductive oil detection sensor made of magnetic nanoparticle material had a higher detection effect, and the signal-to-noise ratio (SNR) of 20-70 μm ferromagnetic particles was increased by 20%-25%. The detection signal-to-noise ratio (SNR) of 80-130 μm non-ferromagnetic particles was increased by 16%-20%. The application of magnetic nanoparticles is a new method in the field of oil detection, which is of great significance for fault diagnosis and the life prediction of hydraulic systems.
液压油或润滑油中的磨损颗粒蕴含着丰富的设备运行信息，是大型机械设备检测和故障诊断的重要依据。本文基于传统的感应式油液检测技术，开发了磁性纳米颗粒。基于磁性纳米颗粒的特性设计了一种新型感应式油液检测传感器。该传感器通过区分铁磁性和非铁磁性磨损颗粒来提高检测灵敏度。磁性纳米颗粒增加了螺线管线圈的内部磁场强度以及螺线管线圈内部磁场的稳定性。实验过程中，首先确定了传感器微通道的最佳位置，然后证实了磁性纳米颗粒对传感器检测的影响，最后确定了混合物的浓度比。实验结果表明，由磁性纳米颗粒材料制成的感应式油液检测传感器具有更高的检测效果，20 - 70μm铁磁性颗粒的信噪比（SNR）提高了20% - 25%。80 - 130μm非铁磁性颗粒的检测信噪比（SNR）提高了16% - 20%。磁性纳米颗粒的应用是油液检测领域的一种新方法，对液压系统的故障诊断和寿命预测具有重要意义。
