锁定放大器
灵敏度(控制系统)
感应式传感器
噪音(视频)
材料科学
放大器
计算机科学
光电子学
电子工程
电气工程
CMOS芯片
工程类
人工智能
图像(数学)
作者
Xiaoxi He,Yuying Li,Zhiqiang Meng,Liuyu Lin,Dihu Chen,Mu-Shui Zhang,Zixin Wang
标识
DOI:10.1016/j.sna.2024.115177
摘要
Metal particles identification in lubrication oil has become a popular technology to perform the fault diagnosis and health monitoring of machinery equipment. Unfortunately, the magnetic signals induced by particles are generally weak or even submerged in heavy noise. The sensitivity of conventional inductive sensors for detecting non-ferrous particles can still be improved to better distinguish between different non-ferrous particles. Based on the above issues, this paper proposed a novel tunnel magnetoresistance (TMR) sensor based on digital lock-in amplifier (DLIA) technology to detect ferrous and multiple nonferrous metallic particles in oil under noisy conditions. Moreover, compared to conventional inductive sensor structures, the proposed method can effectively reduce the sensor volume, which is particularly necessary when considering the design of portable oil multipollutant detection system. Owing to the high resolution of TMR and outstanding versatility of DLIA for extracting weak signal overwhelmed in noise, the detection sensitivity can be improved tremendously. Extensive simulation and experimental results validated that the proposed approach was practical and effective. Based on the experimental results, the minimum values of detectable diameter could be computed as 24.76 µm for iron particles, 81.34 µm and 82.32 µm for copper and aluminium particles respectively.
科研通智能强力驱动
Strongly Powered by AbleSci AI