LC并联对电感式磨损传感器检测精度的影响研究

针对LC并联对传感器的检测性能影响，基于传感器的检测原理，建立了LC并联结构下磨粒引起的传感器输出电动势的数学模型。采用COMSOL仿真软件对不同LC并联结构下的线圈模型进行磨粒检测电磁场仿真，重点对三线圈LC并联结构中检测线圈的并联电容参数进行优化设计。仿真结果表明：激励线圈LC并联谐振下，激励线圈电流随频率变化率增大，线圈轴向磁通密度增强；检测线圈LC并联结构极大增强传感器对磨粒的检测精度，当电容值为7.53 nF时，其输出信号峰峰值最大；三线圈LC并联结构下磨粒引起的输出信号比无电容结构条件下增强了11.65倍。研究结果为高灵敏度电感式磨损传感器的设计提供指导。     

润滑油金属磨粒传感器设计及试验研究

根据电磁感应原理提出一种润滑油金属磨粒传感器模型,并对金属磨粒传感器的基本结构和检测电路进行设计。在传感器测试试验台上对传感器检测抗磁性磨粒和铁磁性磨粒的能力进行测试,研究电压、温度对传感器的影响。测试结果显示:传感器可以准确判别润滑油内的铁磨粒和铜磨粒,其中铁磨粒和铜磨粒的最小检测直径分别为200μm和500μm;在3~5 V电压下该传感器的输出特性稳定;在20~60℃温度范围,温度对传感器的影响可忽略,传感器测量精度满足要求。     

润滑油金属颗粒传感器设计及试验研究

根据电磁感应原理提出一种润滑油金属磨粒传感器模型,并对金属磨粒传感器的基本结构和检测电路进行设计。在传感器测试试验台上对传感器检测抗磁性磨粒和铁磁性磨粒的能力进行测试,研究电压、温度对传感器的影响。测试结果显示：传感器可以准确判别润滑油内的铁磨粒和铜磨粒,其中铁磨粒和铜磨粒的最小检测直径分别为200 μm和500 μm;在3～5 V电压下该传感器的输出特性稳定;在20～60 ℃温度范围,温度对传感器的影响可忽略,传感器测量精度满足要求。     

静电传感器在油液在线监测系统中的应用

将静电传感器技术运用到油液在线监测系统中去,对机械设备的磨损情况进行分析;研究了油液磨粒荷电机制,以及磨粒荷电量的影响因素;提出了一种改进的静电传感器测量模型,利用大型有限元分析软件ANSYS,对影响静电传感器性能的主要因素进行了仿真分析,并对传感器的结构进行了优化设计;研究了磨粒浓度与传感器测量电压之间的关系,运用静电传感器在线监测机械设备润滑油中磨损颗粒的荷电情况,得到设备磨损的监测结果,验证了静电监测方法的有效性。     

低浓度陶瓷CBN砂轮磨削性能分析

浓度是砂轮结构和性能中一个关键指标。为了研究不同浓度对陶瓷CBN砂轮磨削性能的影响,建立了有效磨粒数模型,采用激光位移传感器扫描砂轮表面微观轮廓,量化了不同浓度砂轮的有效磨粒数,并以低浓度砂轮的轴承钢的磨削性能作为判断依据,对有效磨粒数加以对比验证。结果表明:接触磨粒的概率随着砂轮浓度的增大而增大,但浓度低于30%则有效磨粒数过少,磨削性能不佳;浓度达到70%时,有效磨粒数增加不明显。相同配方不同粒度的低浓度砂轮,在合理的相同磨削参数条件下,稍细粒度砂轮可以获得更优的磨削性能。     

电感式磨粒传感器中铁磁质磨粒特性仿真研究

针对机械装置的在线监测传感器,模拟了铁磁质磨粒通过传感器过程中传感器线圈的磁场和感应线圈的感应电压瞬态变化特性.考虑了线圈与铁磨粒的材料、线圈匝数和激励线圈的输入电压等因素,应用Jmag Designer I0.4软件建立了传感器的二维有限元模型.仿真结果揭示了磨粒运动过程中线圈磁场与感应线圈中感应电压的变化规律,获得了感应电压与球形磨粒的直径大小的立方成正比,与磨粒运行速度成正比.研究结果对于电感式磨粒传感器的开发具有重要的指导价值.     

一种基于微流体制备的电阻电感式磨粒传感器

为了实现油液金属磨粒的高精度测量,基于微流体制备了一种可检测电阻电感参数的磨粒传感器。通过仿真获得了金属颗粒在时谐磁场中的磁化和涡流效应特征,并通过实验研究了电阻电感检测的电压特性和频率特性。高频激励可以增强金属颗粒内部的涡流效应,而激励电压对传感器检测结果的影响不大。研究表明电感参数对铁磁性金属的检测能力强,电阻参数对非铁磁性金属的检测能力强。采用2.0 V、2.0 MHz的激励,通过比较分析电阻和电感检测结果,该传感器可有效识别直径60 μm的铜颗粒和直径16 μm的铁颗粒。这种基于线圈电阻参数检测非铁磁性金属磨粒的方法为增强磨粒传感器的综合测量性能提供了新思路。     

电涡流磨粒传感器磁场仿真研究

针对电感式磨粒传感器易受磨粒连续性影响和无法识别磨粒材质的问题,通过增大电感式磨粒传感器中磨粒的涡流作用,提出一种电涡流磨粒传感器。结合有限元软件ANSYS Maxwell建立电涡流磨粒传感器的仿真模型,并对不同材质和尺寸的磨粒进行仿真分析,验证电涡流原理在磨粒监测中的可行性。对不同激励频率及线圈内径的电涡流磨粒传感器进行仿真分析。仿真结果表明:电涡流作用可以识别磨粒尺寸,磨粒产生的涡流作用与磨粒尺寸成三次方关系;电涡流作用可以识别磨粒材质,不同材质的磨粒在相同的磁场环境中产生的涡流作用不同;线圈的激励频率越高,磨粒的涡流作用越大;线圈的内径越大,磨粒的涡流作用越小。     

基于GMR磁传感器的油液铁磁性磨粒在线监测实验研究

为实现对油液中铁磁性磨粒的在线监测,设计搭建一套在线监测装置;基于GMR磁传感器芯片可有效检测微弱磁场的特点,利用GMR磁传感器监测单个铁磨粒被磁化后的剩余磁场;采取"模拟在线"的形式,从GMR磁传感器输出的磁场信号中获取磨粒信息。通过优化装置和试验参数,研究温度、磨粒运动速度对测试系统输出结果的影响,探讨传感器的磨粒检出能力及检出结果的一致性。试验结果显示:在95℃以内的温度和5 cm/s以内的磨粒运动速度条件下,传感器的输出值基本不受影响;运动速度一定时,装置对不同粒度的磨粒输出一致性良好。试验初步表明了GMR磁传感器可用于油液铁磁性磨粒的在线监测,可有效检测出尺寸在75μm以上的铁磨粒。     

一种高梯度静磁场感应式全流量磨粒监测传感器

针对传统单激励油液磨粒传感器难以实现全流量润滑油监测的问题,提出了一种基于多激励结构的感应式磨粒监测传感器。在传感器流道周向均匀布置多个激励结构,扩大检测范围,从而实现全流量磨粒监测。建立三维有限元模型,分析了感应线圈的静磁场和瞬态特性,揭示了激励结构数量与y-z平面周向磁场均匀性的关系。同时,优化了传感器的磁极形状。单颗粒模拟实验表明激励电流、电流方向和激励结构数量直接影响磨粒感应信号波形,验证了激励结构数量是影响传感器有效检测范围的重要因素。润滑油系统循环条件下的磨粒注入实验结果显示,传感器能够在直径为40 mm的流道中检测到13μm的铁磁性磨粒。     

黄铜全流量在线磨粒静电监测实验研究

为深入了解静电监测方法对黄铜的监测能力,采用自制的全流量在线磨粒静电传感器开展黄铜的静电监测方法研究。研究了润滑条件下轴承钢-黄铜滑动摩擦荷电磨粒的产生机理并设计了磨粒静电监测系统,开展了三种尺寸的轴承钢球和黄铜球的单颗粒注入实验、双颗粒注入实验以及相同载荷、不同滑动速度的轴承钢-黄铜滑动摩擦磨损实验,对摩擦因数、静电感应信号、静电信号均方根值进行相关性分析。研究结果表明:全流量在线磨粒静电传感器具有较好的检测一致性;静电监测方法对黄铜的监测能力强于对轴承钢的监测能力;摩擦因数与静电监测信号具有相关性,在磨损阶段,静电感应信号出现脉冲尖峰与持续上升。     

Investigations on the Relationship between Wear Debris Residence Time in Lubrication Systems and Online Oil Sampling Interval

Lubrication systems have significant effects on the residence time of wear debris (RTWD), which limits the monitoring efficiency of the online debris sensor. The focus of this work is to investigate the relationship between RTWD in lubrication systems and online oil sampling interval. Firstly, a vector representation for the attenuation function of wear debris (AFWD) is introduced in order to depict the removal of wear debris with different sizes. Based on this, a mathematical model is developed to calculate the RTWD. A setting criterion for the online oil sampling interval is also proposed. Thereafter, RTWD in different size ranges was investigated experimentally, in which the concentration of wear debris larger than the micrometer rating of the filter decays to 10% of the initial concentration within 5?min. The results were consistent with the proposed model results. Moreover, we find that the online oil sampling interval must be determined by the residence time of large wear debris caused by abnormal wear. Otherwise, the online distortion monitoring results can result in false conclusions.     

Enhancement of oil debris sensor capability by reliable debris signature extraction via wavelet domain target and interference signal tracking

On-line oil debris monitoring is an effective approach to detecting machine component wear through estimating the size and the quantity of metallic debris in the lubricating oil. However, oil debris (particle) signatures are often contaminated by background noise and vibration interference during the operation of the oil debris sensor. As such, the accuracy of debris measurement and counting depends largely on the authenticity of the extracted debris signature. Considering characteristics of both target and interference signals obtained by the oil debris sensor, we propose a novel debris signature extraction technique to improve the oil debris measurement capability based on the wavelet domain information. In each wavelet scale of the oil debris sensor output signal, the debris coefficients are detected based on the singularity of the debris signal. The interference coefficients are estimated by adaptive linear prediction. The overlapped debris and interference coefficients are separated by a new prediction strategy involving alternating applications of forward and backward predictors. The differences between the mixture and the estimated interference coefficients are employed to reconstruct the debris signature. The proposed technique is evaluated using both uni- and bi-excitation experimental data and compared with a recently reported method. The experimental results and comparisons indicate that the proposed new method can extract the debris signature more truthfully, and thus improve the oil debris monitoring accuracy in real applications.     

基于平面线圈的磨粒监测传感器

为了提高油液的在线监测水平,研制了一种基于电感测量的铁磁性颗粒传感器,用MEMS工艺制作了一种平面线圈型的传感器并分析了其工作原理.设计了信号处理电路,对传感器的性能进行了实验研究.结果表明:颗粒含量在10～ 100 mg时,传感器具有良好的线性度,灵敏度为7.145 5 V/g,该传感器可以分辨5 mg的铁屑含量,输出稳定,为油液的在线监测提供了一种新的检测元件.     

A high throughput inductive pulse sensor for online oil debris monitoring

A high throughput inductive pulse sensor based on inductive Coulter counting principle for detecting metallic wear debris in lubrication oil is presented. The device detects the passage of metallic debris by monitoring the inductance change of a two-layer planar coil with a meso-scale fluidic pipe crossing its center, which is designed to attain high throughput without sacrificing the sensitivity. The testing results using iron and copper particles ranging in size from 50 to 150 μm have demonstrated that the device is capable of detecting and distinguishing ferrous and non-ferrous metallic debris in lubrication oil with a high throughput.     

Intelligent identification of wear mechanism via on-line ferrograph images

Condition based maintenance（CBM） issues a new challenge of real-time monitoring for machine health maintenance. Wear state monitoring becomes the bottle-neck of CBM due to the lack of on-line information acquiring means. The wear mechanism judgment with characteristic wear debris has been widely adopted in off-line wear analysis; however, on-line wear mechanism characterization remains a big problem. In this paper, the wear mechanism identification via on-line ferrograph images is studied. To obtain isolated wear debris in an on-line ferrograph image, the deposition mechanism of wear debris in on-line ferrograph sensor is studied. The study result shows wear debris chain is the main morphology due to local magnetic field around the deposited wear debris. Accordingly, an improved sampling route for on-line wear debris deposition is designed with focus on the self-adjustment deposition time. As a result, isolated wear debris can be obtained in an on-line image, which facilitates the feature extraction of characteristic wear debris. By referring to the knowledge of analytical ferrograph, four dimensionless morphological features, including equivalent dimension, length-width ratio, shape factor, and contour fractal dimension of characteristic wear debris are extracted for distinguishing four typical wear mechanisms including normal, cutting, fatigue, and severe sliding wear. Furthermore, a feed-forward neural network is adopted to construct an automatic wear mechanism identification model. By training with the samples from analytical ferrograph, the model might identify some typical characteristic wear debris in an on-line ferrograph image. This paper performs a meaningful exploratory for on-line wear mechanism analysis, and the obtained results will provide a feasible way for on-line wear state monitoring.     

High Throughput Wear Debris Detection in Lubricants Using a Resonance Frequency Division Multiplexed Sensor

With a long time goal of detecting signs of potential machine failure, we demonstrate a proof-of-principle multiplexed, multichannel, inductive pulse sensor based on resonant frequency division multiplexing for high throughput detection of micro-scale metallic debris in lubricants. In the four-channel sensor, each sensing coil is connected to a specific external capacitance to form a parallel LC circuit that has a unique resonant frequency. Only one combined sinusoidal excitation signal consisting of four frequencies components that are close to the 4 sensing channels’ resonant frequencies was applied to the sensor, and only one combined voltage response was measured. Because each sensing channel exhibited a peak amplitude at its resonant frequency, the signals for each individual channel were recovered from the combined response by taking the spectrum components at each resonant frequency with an improved signal-to-noise ratio. Inductance change for each channel was then calculated from signals of individual channels. Testing results show that the use of resonant frequency division multiplexing allows simultaneous detection of debris in lubricants using only one set of detection electronics; for the four-channel sensor, there is a 300 % increase in throughput. The resonant frequency division multiplexing concept can be potentially applied to a multichannel oil debris sensor with a large number of sensing channels to achieve a very high throughput, which is necessary for online health monitoring of rotating and reciprocal mechanical components.     

Theoretical characterization of a gas path debris detection monitoring system based on electrostatic sensors and charge amplifiers

The electrostatic detection of gas path debris has been proposed in literature as a tool to monitor mechanical component faults along the combustion gas path of turbo-machines. In this paper we discuss the general theoretical characterization of a debris detection monitoring system based on electrostatic sensors and charge amplifiers. We first provide the analytical expression of the time-varying charge induced by the moving debris on the sensor surface. After introducing a simplified electric model of the charge amplifier, we discuss the effects of the amplifier bandwidth on the shape of the output voltage signal. Theoretical results have been validated with experiments.