基于电容传感的滑油磨粒监测技术研究进展

航空发动机内部旋转部件等区域的磨损,是造成其故障的重要原因.磨损所产生的磨粒是旋转部件磨损状况的重要信息载体,对滑油磨粒进行在线监测,不仅能够掌握滑油系统的健康状态,而且能诊断和评价滑油流经的旋转部件的磨损状况.电容传感器因其结构简单、集成度高、灵敏度高等优势,在滑油监测领域具有应用前景.主要介绍了基于电容测量原理的不同类型传感器及相应的检测技术,并论述了不同类型电容传感技术的优缺点,综述了基于电容传感的滑油监测技术的研究现状.在此基础上展望了滑油磨粒电容传感技术未来发展方向,提出应从环境因素、多维度信号特征提取、磨粒监测收集分析一体化和预测发动机损伤情况等方面进行重点研究,旨在进一步提高滑油磨粒电容传感技术的成熟度以实现工程应用.     

灰色定权聚类在磨粒识别中的应用

引入了一套磨粒的形成学参数描述体系，实现了磨损颗粒图象的数值描述，并将灰色系统中的定权聚类技术用于磨损颗粒的自动识别，编制了相应的计算机模拟程序。在识别过程中根据某型航空发动机的滑油磨粒监测试验，确定了磨损颗粒各形态参数的聚类权值和灰类白化权函数。应用此方法对一组测试磨粒进行了模拟识别，识别正确率在90以上，并且识别速度很快，大大优于传统的磨粒识别方法。     

基于小波变换的油液金属颗粒检测算法研究

为了实时掌握发动机滑油系统的磨损状况,需要在线监测系统连续监测发动机内部磨损情况.随着发动机的工作时间与磨损状态的不同,其金属磨粒的浓度、成分、尺寸等参数均发生不同的变化.因此,滑油油液中金属颗粒参数的变化可以作为重要指标来反映发动机的磨损状态,需要专门的传感器系统进行实时在线监测.但由于使用环境等因素影响,往往会使油液金属颗粒信号包含噪声,对信息检测造成影响.提出一种基于小波变换的油液金属颗粒检测算法,充分利用小波变换对时域和频域的良好局部化性质以及多分辨率分析的特点,并结合发动机实际工况下的信号特征对信号进行降噪及检测,并对算法进行了实验验证.结果表明,该算法可以有效地实现信号降噪及检测.     

船舶动力与传动装置润滑油状态监测

针对目前舰船动力系统故障定位难、预判不准确的现状,提出了一种应用于船舶动力与传动装置的油液在线监测技术。通过分析舰船动力系统的故障源,阐述了船舶动力与传动装置滑油状态监测的内容,包含油液黏度检测、油液水分检测与油液金属磨粒检测。结合黏度、水分与磨粒检测原理,设计了滑油状态监测试验。通过实船试验,验证了油液在线监测技术在船舶动力与传动装置状态监测中应用的可行性和有效性。     

磨粒形态对滑油屑末监测器输出特性的影响

滑油屑末传感器基于磁场微平衡技术,能定量区分铁磁性和非铁磁性颗粒,可以为大型旋转部件健康监测提供重要数据.基于COMSOL建立有限元模型,分析了微平衡磁场下不同椭球磨粒和圆柱磨粒磁化场,并采用ANSYS Maxwell建立滑油屑末传感器模型,研究了不同长径比圆柱磨粒通过滑油屑末传感器时信号强度的变化.结果 表明,铁磁性颗粒经过平衡磁场时,轴线位置上磨粒中心处磁感应强度最大,随着离中心点距离增加磁感应强度衰减;切线位置上磁感应强度随着空间位置变化而改变,在切点处最小.对于不同的铁磁性颗粒形态,当磨粒体积相同时,长径比越大磁感应强度越大,信号强度越大.同时,搭建了滑油屑末测试系统,验证了有限元分析结果的正确性.     

基于2D熵阈值的铁谱磨粒图像分割方法

铁谱磨粒图像分割是磨粒自动识别的重要环节,其分割效果直接影响磨粒识别精度。铁谱磨粒形态千变万化,很难找到一种通用性强、算法快、精度高的分割算法,磨粒图像分割甚至比磨粒识别还困难,是磨粒自动识别与铁谱推广使用的瓶颈之一。该文基于二维直方图、二维熵概念,提出了一种快速二维熵阈值的磨粒图像分割算法,经实际工程应用表明,该算法具有很好的分割效果。     

基于电容传感器的飞机滑油磨粒检测系统设计

飞机发动机的异常摩擦、磨损会在滑油中产生大量的磨粒,对滑油磨粒的有效检测可以为发动机的故障诊断提供可靠信息。设计了一个弧状极板式电容传感器,但传感器中电容变化量非常小,传统方法难以检测,采用交流电桥式电容检测方法。系统以AD9833为DDS信号发生器给交流电桥施加激励信号,经C/V转换、差分放大、相敏解调以及低通滤波等处理后得到实时电压变化。可实现弱信号的精确检测,且能有效克服杂散电容和寄生电容的影响。多次实验结果均表明：该检测系统具有灵敏度高,稳定性好等特点。     

基于集成智能算法的发动机滑油系统融合诊断

为确保飞行训练安全、预防发动机维修事故发生,针对当前航空发动机磨损故障诊断存在的问题,提出基于集成智能算法的发动机滑油系统故障检测模型.从铁谱诊断、光谱诊断、颗粒计数诊断、理化指标诊断和试车台数据诊断等方面研究了运用集成神经网络方法对多源信息进行融合诊断.结果表明:融合诊断结果的故障模式比单项诊断结果的故障模式要多;当单项诊断出现矛盾时,融合诊断结果能很好地解决诊断冲突问题.基于集成神经网络的发动机油液系统磨损故障的融合诊断能充分利用多种方法的互补性和有效解决诊断冲突问题,从而使诊断结果更为可靠和准确.     

一种磨粒在线监测传感器的设计及其特性分析

磨损失效占机器零件的失效形式中的 70 %以上 ,磨粒隐含着机械装备运行状态的大量信息 ,而大磨损金属颗粒是加速大型机械设备磨损的重要因素。目前 ,我国在大磨损金属颗粒在线检测研究方面还比较欠缺。本文应用电磁感应监测原理 ,设计分析了一种螺线管式磁感应大磨损金属颗粒在线监测传感器 ,并用 ANSYS电磁 -电路耦合场仿真该传感器的监测机理 ,验证了设计原理的可行性 ;确定了影响传感器性能的关键因素 ,同时确定了传感器的几组几何尺寸和性能参数 ,为该种传感器的研制奠定了良好基础。     

金属磨粒分形参数的计算

为了便于分析研究设备的磨损状态等相关性规律，研制了基于计算机的磨粒图像轮廓识别和轮廓分形参数分析软件。在获取磨粒图像链码的基础上，利用该软件对销一盘试验机采集的磨粒进行分析，发现磨粒分形维数的变化与磨损状态有一定对应关系。该软件为磨粒分形特征与磨损状态相关性规律的研究，提供了简便快捷的手段。     

Knowledge-based diagnosis of drill conditions

One major bottleneck in the automation of the drilling process by robots in the aerospace industry is drill condition monitoring. This paper describes a system approach to solve this problem through the advancement of new machine design, sensor instrumentation, metal-cutting research, and intelligent software development. All drill failures can be detected and distinguished: chisel edge wear, flank wear, crater wear, margin wear, corner wear, breakage, asymmetry, lip height difference, and chipping at lips. However, in the real manufacturing environment, different workpiece materials, drill size, drill geometry, drill material, cutting speed, feed rate, etc. will change the criteria for judging the drill condition. The knowledge base used for diagnosing the drill failures requires a huge data bank and prior exhaustive testing. A self-learning scheme is therefore introduced to the machine in order to acquire the threshold history needed for automatic diagnosis by using the same new tool under the same drilling conditions.     

Force based tool wear monitoring system for milling process based on relevance vector machine

The monitoring of tool wear status is paramount for guaranteeing the workpiece quality and improving the manufacturing efficiency. In some cases, classifier based on small training samples is preferred because of the complex tool wear process and time consuming samples collection process. In this paper, a tool wear monitoring system based on relevance vector machine (RVM) classifier is constructed to realize multi categories classification of tool wear status during milling process. As a Bayesian algorithm alternative to the support vector machine (SVM), RVM has stronger generalization ability under small training samples. Moreover, RVM classifier results in fewer relevance vectors (RVs) compared with SVM classifier. Hence, it can be carried out much faster compared to the SVM. To show the advantages of the RVM classifier, milling experiment of Titanium alloy was carried out and the multi categories classification of tool wear status under different numbers of training samples and test samples are realized by using SVM and RVM classifier respectively. The comparison of SVM with RVM shows that the RVM can get more accurate results under different number of small training samples. Moreover, the speed of classification is faster than SVM. This method casts some new lights on the industrial environment of the tool condition monitoring.     

盾构机机械类故障诊断研究进展综述

盾构机在运行过程中不可避免地会发生故障,这些故障中以机械类故障较为常见,对盾构机进行状态监测与故障诊断是保证其掘进作业安全、高效的重要手段。详细论述了盾构机刀具磨损、轴承、液压等常见机械类故障及相应的诊断方法,分析了各类故障诊断存在的问题并提出了相应的解决方案。最后对盾构机机械类故障诊断技术进行了建议与展望,并指出在故障诊断系统生态技术框架下,实现盾构机机械类故障诊断的信息化和智能化将会是盾构机未来的发展目标。     

Diagnostics and forecasting of cutting tool wear at CNC machines

The problem of monitoring and forecasting the remaining cutting tool durability is formulated and an architectural model of a generalized diagnostic system and its software implementation are suggested. A diagnostic module/CNC system kernel protocol is specified and a universal solution to diagnosing and predicting cutting tool wear is presented being based on an external calculator.     

基于图像连通区域数的刀具磨损状态特征提取实验研究

为了利用计算机视觉技术进行刀具状态监测,设计了机械加工刀具状态监测实验系统,并通过将图像处理技术引入到机械加工刀具磨损状态监测中,提出了一种通过提取工件表面图像的连通区域数来判断刀具磨损状态的新方法。该方法首先采集被加工工件的表面图像;然后对图像进行预处理,并对区域行程算法进行了改进,再用改进的区域行程标记算法对机械加工工件表面图像进行标记;最后通过统计连通区域数来判断刀具的磨损状态。理论和实验分析表明,由于加工工件表面图像的连通区域数和刀具磨损有很强的相关性,其可以间接判断刀具磨损情况,从而可达到对刀具状态进行监测的目的。实验表明,该方法计算简单、识别速度快,可以有效地判断刀具的磨损状态。     

Calibration-based tool condition monitoring for repetitive machining operations

Reliable tool condition monitoring (TCM) system is essential for any machining process in mass production to control the part quality as well as reduce the machine tool downtime and maintenance costs. However, while various research studies have proposed their TCM systems, the complexity in setups with advanced decision-making algorithms and specificity in application to limited cutting conditions continue to complicate the implementation of these systems into practical scenarios. This study develops a very simple and flexible TCM system for repetitive machining operations. The proposed monitoring approach reduces the complexity of monitoring model by considering the important characteristic of repeatability in process which has been commonly found in the mass production scenario and implements the calibration procedure to improve the flexibility of the model application to actual machining processes with complex toolpath designs and variable cutting conditions. The selected cutting tools with specific tool conditions are used in the calibration phase to generate reference signals. In actual repetitive production, the collected signal generated by the cutting tool in each operation is compared with reference signals to identify the most similar condition of the reference tool through the proposed similarity analysis. To validate the performance, the current study demonstrates the application of proposed monitoring approach to monitor the tool wear in repetitive milling operations with complex toolpath, and the predicted tool wear progression is found to be in good agreement with experimental measurements during the machining of multiple parts over the entire tool life.     

Tool wear estimation using an analytic fuzzy classifier and support vector machines

A new type of continuous hybrid tool wear estimator is proposed in this paper. It is structured in the form of two modules for classification and estimation. The classification module is designed by using an analytic fuzzy logic concept without a rule base. Thereby, it is possible to utilize fuzzy logic decision-making without any constraints in the number of tool wear features in order to enhance the module robustness and accuracy. The final estimated tool wear parameter value is obtained from the estimation module. It is structured by using a support vector machine nonlinear regression algorithm. The proposed estimator implies the usage of a larger number and various types of features, which is in line with the concept of a closer integration between machine tools and different types of sensors for tool condition monitoring.     

Multisensory fusion based virtual tool wear sensing for ubiquitous manufacturing

Pervasiveness of ubiquitous computing advances the manufacturing scheme into a ubiquitous manufacturing era which poses significant challenges on sensing technology and system reliability. To improve manufacturing system reliability, this paper presents a new virtual tool wear sensing technique based on multisensory data fusion and artificial intelligence model for tool condition monitoring. It infers the difficult-to-measure tool wear parameters (e.g. tool wear width) by fusing in-process multisensory data (e.g. force, vibration, etc.) with dimension reduction technique and support vector regression model. Different state-of-the-art dimension reduction techniques including kernel principal component analysis, locally linear embedding, isometric feature mapping, and minimum redundancy maximum relevant method have been investigated for feature fusion in a virtual sensing model, and the kernel principal component analysis performs best in terms of sensing accuracy. The effectiveness of the developed virtual tool wear sensing technique is experimentally validated in a set of machining tool run-to-failure tests on a computer numerical control milling machine. The results show that the estimated tool wear width through virtual sensing is comparable to that measured offline by a microscope instrument in terms of accuracy, moreover, in a more cost-effective manner.     

A sensor fusion and support vector machine based approach for recognition of complex machining conditions

During the machining process of thin-walled parts, machine tool wear and work-piece deformation always co-exist, which make the recognition of machining conditions very difficult. Existing machining condition monitoring approaches usually consider only one single condition, i.e., either tool wear or work-piece deformation. In order to close this gap, a machining condition recognition approach based on multi-sensor fusion and support vector machine (SVM) is proposed. A dynamometer sensor and an acceleration sensor are used to collect cutting force signals and vibration signals respectively. Wavelet decomposition is utilized as a signal processing method for the extraction of signal characteristics including means and variances of a certain degree of the decomposed signals. SVM is used as a condition recognition method by using the means and variances of signals as well as cutting parameters as the input vector. Information fusion theory at the feature level is adopted to assist the machining condition recognition. Experiments are designed to demonstrate and validate the feasibility of the proposed approach. A condition recognition accuracy of about 90 % has been achieved during the experiments.     

Distributed fault detection in industrial system based on sensor wireless network

In metal cutting processes, an effective monitoring system, based on a suitably developed scheme or set of algorithms can maintain machine tools in good condition and delay the occurrence of tool wear. In this paper, an approach is developed for fault detection based on a distributed system. Firstly, identifying of sensor instrumentation system is responsible for the signal processing and the system fault information. Secondly, the sensor wireless networks are used to transmit the data (lower layer) to or receive the commands from the computer center (top layer). Thirdly, the computer center at the top layer will monitor the overall system and generate the alarm signals or the commands when the faults occur.