三线圈电感式油液金属磨粒检测系统

机械传动金属磨粒可以反馈机械设备故障的特性,针对机械传动系统故障在线检测需求,提出了一种三线圈电感式金属磨粒检测系统;通过建立传感器数学模型,对影响传感器金属探测灵敏度的参数进行仿真,结合传感器实际使用情况求出最优解;通过设计励磁信号源模块、调幅模块、信号调理模块等电路并结合上位机对油液中的金属磨粒进行检测;经孔径为8 mm的流道的实验测试,实现了500μm铁磨粒及1 000μm铜磨粒的检测精度;该系统为发动机中金属磨粒的检测提供了技术支持,对故障预防与诊断具有重要意义。     

航空发动机旋转部件磨粒监测技术的进展

航空发动机在使用中会发生一些因旋转部件磨损引起的故障。航空发动机因磨损引起的故障发生频率高、危害严重,为了飞行安全,必须及时准确地检测发动机旋转部件磨损状态。应用实践证明,油液磨粒监测技术能够提前预报装备磨损故障,降低维修成本,延长装备使用寿命。介绍了航空发动机旋转部件磨损监测的常用技术以及近些年出现的新技术,并对这些技术的特点做了分析与对比,指出了各类技术的适用范围。     

航空发动机传感器故障诊断

研究航空发动机传感器故障诊断问题,由于发动机传感器故障样本有限、小样本、非线性变化特点,传统大样本传统故障方法故障诊断准确率低。为提高传感器故障诊断准确率,提出一种混沌粒子群算法(CPSO)和最小二乘支持向量机(LSS-VM)相结合的传感器故障诊断算法(CPSO-LSSVM)。首先将发动机传感器信号输入到LSSVM进行学习,并采用CPSO进行优化,找到最优LSSVM参数,从而建立传感器故障诊断模型,最后采用已建立模型对传感器故障进行仿真测试。仿真结果表明,CPSO-LSSVM提高了航空发动机传感器故障诊断的准确率,能准确地对空发动机传感器故障进行诊断,提供民飞行安全性能保障。     

基于电介质介电常数测量的油液在线监测技术研究

润滑油的劣化过程蕴含着发动机的故障信息,在这一过程中其介电常数会有对应的变化.提出了一种基于电介质介电常数测量的油液在线监测方法,并对监测原理进行了介绍,设计了油液在线监测系统包括电容传感器、微小电容变化量检测电路及相应的测试分析软件.对不同污染度的润滑油进行了检测实验,结果表明该系统能够准确有效地监测油液的介电常数的变化,可为发动机更换润滑油及故障诊断提供参考依据.     

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.     

电涡流式油液磨粒监测传感器的研究

近年来,油液监测技术领域的研究和开发的热点集中在油液在线监测方法上。为此提出一种电涡流式基于PCB平面线圈传感器的油液磨粒监测方法,首先通过电磁仿真软件Maxwell进行平面线圈的电磁仿真,获得最优PCB平面线圈的结构设计;然后采用电桥法设计了传感器测量电路,运用模数转换原理设计了信号调理电路;最后制备了此传感器。经过试验,其结果表明:该传感器具有良好的线性度及灵敏度。该研究为微型传感器加入油液磨粒监测技术提供一种可行的方法,不仅缩小了传感器体积,而且降低了监测成本。     

基于滑模观测器和神经网络的传感器故障诊断方法比较研究

针对航空发动机传感器故障诊断中各种方法的优势和劣势,选择滑模观测器和神经网络这两种故障诊断方法分别对航空发动机转速传感器进行故障诊断研究,采用实验室搭建的发动机实验台DGEN380的实验数据,选择对航空发动机控制系统影响较大的偏置故障、漂移故障、脉冲故障、周期性干扰故障这四类传感器故障进行诊断。研究结果表明,滑模观测器和IPSO-BP神经网络都能实现航空发动机传感器的故障诊断;滑模观测器方法可以诊断出偏置故障、脉冲故障和周期性干扰故障,但不能诊断出传感器发生的漂移故障;IPSO-BP神经网络方法可以诊断出偏置故障、漂移故障、脉冲故障和周期性干扰故障。因此,滑模观测器在故障诊断中可能会出现漏诊的现象,IPSO-BP神经网络相对滑模观测器而言不会出现漏诊的现象。     

基于主元分析法的航空发动机传感器故障诊断研究

主要研究了主元分析方法在航空发动机传感器故障诊断中的应用,并提出了主元分析法故障诊断算法。假设只有传感器故障情况下,将传感器测量值所组成的测量空间分解为主元和残差两个子空间,并通过传感器实际测量数据与正常数据矩阵在残差空间上的投影做比较,对传感器故障进行故障诊断;针对航空发动机的压力温度转速等传感器常见的故障,通过运行故障仿真平台绘制了其多元统计特征图;分析仿真结果表明,主元分析法对航空发动机传感器具有很好的故障检测和故障诊断能力。     

新型航空发动机测试传感器的发展趋势

新一代航空发动机对测试传感器提出了新的要求,特别是能够在高温环境下正常工作的能力。现有的航空发动机测试传感器已经不能满足该条件,因此迫切需要发展新的技术。概述了可应用于航空发动机测试的新原理及新技术,如MEMS技术、光谱技术等,并详细介绍了燃油品质、温度、叶尖间隙、排放物检测等新型航空发动机测试传感器的一些进展及其发展趋势。     

涡轮后排气温度摆动故障仿真研究

介绍了一种航空发动机低压涡轮后排气温度Tt摆动故障仿真研究方法,分析了导致航空发动机低压涡轮后排气温度摆动故障的典型原因.应用虚拟仪器编程技术,采用模块化编程思想,建立了低压涡轮后排气温度控制系统仿真平台,实现了实际试车过程低压涡轮后排气温度Tt摆动的故障仿真.在传感器信号回路串入低通滤波器进行滤除干扰仿真,根据仿真结果提出了排除Tt摆动故障的方法.经试车验证,此仿真平台能有效指导航空发动机低压涡轮后排气温度控制系统故障排除.     

油液在线监测技术研究进展

油液在线监测技术是机械故障智能诊断的重要发展方向,传感器作为其关键元件,发挥着重要作用。对油液在线监测的粘度传感器、水分传感器、磨粒传感器以及多传感器集成的国内外研究进展进行了介绍。从电磁法、静电法、电感法、图像识别几个方面对磨粒测量与分析的国内外研究进行了介绍。提出了现阶段存在的不足,对油液在线监测的未来发展之路进行了展望。     

磨粒径向分布对电感式磨粒传感器测试结果的影响

根据电感平衡原理,设计了用于磨粒在线测量的电感式磨粒传感器.分析了传感器的测试原理和磨粒通过传感器线圈时径向分布对测试结果的影响.通过计算线圈内测试面的磁场分布,提出了提高线圈测试面磁场均匀性的设计准则.建立了磨粒位置偏离线圈中心时,磨粒磁化场的磁通求解模型.模型计算结果表明,磨粒径向位置的改变,使得线圈各横截面上磁化场的磁通发生了变化.当线圈达到一定长度后,磁化场的磁链变化很小.因此在保证传感器线圈测试面磁场均匀性和线圈长度的前提下,磨粒径向分布对测试结果的影响可忽略.研究结论为分析电感式磨粒传感器测试结果一致性和优化传感器的结构设计提供了理论依据.     

FADEC硬件在回路测试系统设计

发动机全权限数字电子控制器(FADEC)对航空发动机进行实时在线控制,其控制效果直接影响发动机的安全运行.硬件在回路测试系统是FADEC设计与验证的重要手段之一.设计了一种FADEC硬件在回路测试系统,该系统包括发动机电子控制器(EEC)、接口模拟器、发动机和飞机模型及其显示系统.该系统可以实时模拟发动机和飞机的各种传感器、执行机构信号,对FADEC进行高效、全面的验证,主要包括控制规律、EEC的接口电路、自检测、容错和重构策略等.通过大量的试验测试表明,该系统能够实现实时、稳定、高效的闭环仿真,达到了验证控制算法和控制逻辑的要求.     

Aero-Engine Surge Fault Diagnosis Using Deep Neural Network

Deep learning techniques have outstanding performance in feature extraction and model fitting. In the field of aero-engine fault diagnosis, the introduction of deep learning technology is of great significance. The aero-engine is the heart of the aircraft, and its stable operation is the primary guarantee of the aircraft. In order to ensure the normal operation of the aircraft, it is necessary to study and diagnose the faults of the aero-engine. Among the many engine failures, the one that occurs more frequently and is more hazardous is the wheeze, which often poses a great threat to flight safety. On the basis of analyzing the mechanism of aero-engine surge, an aero-engine surge fault diagnosis method based on deep learning technology is proposed. In this paper, key sensor data are obtained by analyzing different engine sensor data. An aero-engine surge dataset acquisition algorithm (ASDA) is proposed to sample the fault and normal points to generate the training set, validation set and test set. Based on neural network models such as one-dimensional convolutional neural network (1D-CNN), convolutional neural network (RNN), and long-short memory neural network (LSTM), different neural network optimization algorithms are selected to achieve fault diagnosis and classification. The experimental results show that the deep learning technique has good effect in aero-engine surge fault diagnosis. The aero-engine surge fault diagnosis network (ASFDN) proposed in this paper achieves better results. Through training, the network achieves more than 99% classification accuracy for the test set.     

航空发动机综合调节器检测系统的设计

通过对现有检测设备使用情况调研和问题分析,总结维修使用人员的检测需求,根据航空发动机综合调节器的信号特征,设计了一种基于虚拟仪器的发动机综合调节器检测系统,开发了用于调节器各通道的原位检测和离线检测的软件系统。硬件部分采用NI提供的Compact RIO平台,较好地实现了检测系统的开放性和一体化设计,对离线检测部分进行了传感器的模拟。系统实现了对调节器的参数标校、通道测试和故障诊断等功能。试验表明,该检测系统不仅检测数据准确、可靠,而且功能全面、操作简单,达到了设计目标。     

基于ANSYS的在线屑末传感器特性研究

滑油中金属屑末的数量和种类反映了发动机润滑部件的磨损情况,对其进行监测可以预测早期故障和减少维修费用。为了获取在线屑末传感器的输出特性,采用电磁感应原理对不同种类金属屑末的检测机理进行研究。利用ANSYS软件建立传感器模型,着重分析了滑油中不同尺寸金属屑末的感应特性。建立了不同种类金属屑末的尺寸与传感器输出信号的映射曲线,为传感器的标定提供了理论依据。采用标准金属屑末进行了传感器输出测试,实验结果表明传感器的输出与金属屑末半径的幂函数成正比。     

航空发动机传感器故障鲁棒检测方法

研究发动机传感器故障准确检测问题,现代航空发动机数字电子控制系统对传感器的可靠性要求日益提高。针对航空发动机结构复杂,又工作在高温和高压下,常规采用的传感器故障检测方法的准确性易受到建模误差与外界扰动的影响,造成漏报或误报。为了提高检测精度,提出建立航空发动机数控系统传感器未知输入故障模型,采用特征结构配置的方法,通过配置闭环系统左特征向量实现故障检测残差对不确定性因素的干扰解耦,降低扰动对故障诊断结果的影响。用某型涡扇发动机数控系统传感器故障数字仿真试验表明,所设计的方法对范数有界的不确定量可以实现干扰解耦,抑制干扰对故障检测的影响,改善检测算法的鲁棒性,提高检测结果的准确性,同时满足在线运算的实时性要求。提高了航空发动机的可靠性,保证了安全飞行。     

Object extraction from an image of wear particles on a complex background

The object extraction of a debris image is an important basic task in identifying wear particles in ferrographic analysis. However, there is some difficulty in object extraction because of noise jamming in the original debris image. In the present study, two methods of image enhancement—weighted mean filtering and adaptive median filtering—were applied in order to improve the image quality. Then, the adaptive thresholding selection method was used, which is based on an improved debris image. Finally, the effective segmentation of the debris image and the automatic extraction of debris objects were realized. At the same time, targetting the characteristics of low proportion of an object in the total image, a novel method of adaptive thresholding selection was put forward, which is based on the Ostu thresholding method. The segmentation results along with the debris image prove that the current method can give more precise and accurate segmentation of objects than the classical methods. The results also showed that methods in the present paper were concise and effective, which provides an important basis for the further study of debris recognition, fault diagnosis, and condition monitoring of machines. The text was submitted by the authors in English. Xianguo Hu (born 1963), PhD, is a professor at the School of Mechanical and Automotive Engineering at the Hefei University of Technology, China. He received his BS and MS in Powder Metallurgy Material and Mechanics (Tribology) from the Hefei University of Technology in 1985 and 1988, respectively. His PhD degree was awarded at Szent Istvan University, Hungary, in 2002. As a visiting scientist, he conducted research at the Technical University of Budapest, Hungary, and the Technical University of Berlin, Germany, from 1994 to 1997. His research areas include wear debris analysis, optimal tribological design, friction and wear mechanisms, etc. He is the author or coauthor of more than 100 published technical papers. Peng Huang (born 1981) is an MS student at the School of Mechanical and Automotive Engineering of Hefei University of Technology, China. His main focus is on wear debris analysis. Shousen Zheng (born 1963) is an associate professor at the School of Engineering, SunYat-Sen University, China. He received his BS, MS, and PhD in Mechanical Engineering from Hefei University of Technology in 1985, 1988, and 2001, respectively. From 1988 to 2004, he was employed at the Department of Mechanical Engineering at the Hefei University of Technology. In 2005, he moved to the current university. His research interests include computer language, auto CAD/CAM, wear debris analysis, etc. He is the author or coauthor of more than 40 published technical papers.