• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2021, Vol. 57 ›› Issue (12): 126-135.doi: 10.3901/JME.2021.12.126

• 仪器科学与仪器 • 上一篇    下一篇

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金属材料表面残余应力超声测量方法

牛泽, 李凯, 白文斌, 孙圆圆, 龚卿青, 韩焱   

  1. 中北大学信息探测与处理山西省重点实验室 太原 030051
  • 收稿日期:2020-08-06 修回日期:2021-02-19 出版日期:2021-08-31 发布日期:2021-08-31
  • 通讯作者: 韩焱(通信作者),男,1957年出生,博士,教授,博士研究生导师。主要研究方向为信号处理和识别、探测与成像、无损检测等。E-mail:hanyan@nuc.edu.cn
  • 作者简介:牛泽,男,1997年出生。主要研究方向为油液监测及信号处理。E-mail:s1805031@st.nuc.edu.cn
  • 基金资助:
    山西省自然科学基金(201801D121156)和山西省重点研发计划(201903D121133)资助项目

Design of Inductive Sensor System for Wear Particles in Oil

NIU Ze, LI Kai, BAI Wenbin, SUN Yuanyuan, GONG Qingqing, HAN Yan   

  1. Shanxi Provincial Key Laboratory of Information Detection and Processing, North University of China, Taiyuan 030051
  • Received:2020-08-06 Revised:2021-02-19 Online:2021-08-31 Published:2021-08-31

摘要: 油液中的磨粒可反映发动机等设备的磨损状况,为实现油液金属磨粒的在线监测,基于电磁感应原理建立了三线圈传感器的数学模型,通过仿真分析传感器最佳结构参数(内径、间隙、宽度等),利用相干解调模型提取磨粒信号,并分析磨粒信号产生原理。系统采用多层屏蔽结构,可有效减少外部的磁场干扰,设计的传感器检测系统接入风机齿轮箱油路进行相关试验。试验结果表明,本系统可对磨粒信号进行有效提取,且磨粒信号同时受磨粒速度及磨粒尺寸的影响,可在流量为1~18 L/min的工况下实现187 μm铁磁性金属磨粒和578 μm非铁磁性金属磨粒的检测,后续可结合BP神经网络对油液金属磨粒各特征参数进行自适应判别,对今后油液磨粒在线监测设备的开发提供了理论支撑及技术支持,为机械设备故障诊断提供重要依据。

关键词: 油液, 磨粒检测, 相干解调, 多层屏蔽

Abstract: The wear particles in oil can reflect the wear condition of engine and other equipment. In order to realize the on-line monitoring of metal wear particles in oil, a mathematical model of three coil sensor is established based on the principle of electromagnetic induction. The optimal structural parameters of the sensor (inner diameter, gap, width, etc.) are analyzed by simulation. The coherent demodulation model is used to extract the wear particle signal, and the generation principle of wear particle signal is analyzed. The system adopts multi-layer shielding structure, which can effectively reduce the external magnetic field interference. The designed sensor detection system is connected to the oil circuit of the fan gearbox for relevant experiments. The experiments show that the system can effectively extract the wear particle signal, and the wear particle signal is affected by the wear particle speed and size at the same time. It can realize the detection of 187 μm ferromagnetic metal wear particles and 578 μm non ferromagnetic metal wear particles under the flow rate of 1-18 L/min. Subsequently, BP neural network can be used to identify the characteristic parameters of oil metal wear particles adaptively, which provides theoretical and technical support for the development of oil wear particles on-line monitoring equipment in the future, and provides an important basis for fault diagnosis of mechanical equipment.

Key words: oil, wear particle detection, coherent demodulation, multilayer shielding

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