燃气轮机滚动轴承故障模式及延寿方法

分析了燃气轮机滚动轴承常见的10种故障模式及其产生原因,研究了相应的延寿方法及其延寿机理。     

超声速叶型前缘几何形状对叶栅气动性能的影响

应用NURBS技术构造超声速叶型,采用两个控制参数调整叶栅前缘椭圆弧的几何形状,来探索不同的前缘构型对超声速叶型气动性能的影响效应;通过对比分析调整每个参数所得的一系列叶栅的流场计算结果,发现椭圆弧的形状控制因子对超声速叶栅的前缘激波和气流流动状况具有一定的改善效果,同时在保证叶栅气动弦长基本不变的情况下,存在某个椭圆弧形状控制因子和方向控制因子的匹配能够使叶栅的气动性能达到最佳.     

The role of AVDR in linear cascade testing

Linear cascade testing plays an important role in the research and development of turbomachinery and is widely used over the world.The ideal cascade model of a turbomachinery blade row is two-dimensional.In actual linear cascade testing, the flow through the test section converges due to the development of the boundary layer and secondary flow along the sidewall surfaces of the test section.Axial velocity density ratio(AVDR) is adopted to account for the deviation of the tested cascade flow from the ideal 2D model.Among numerous published cascade works, the influence of AVDR on cascade performance is seen to be complicated with many affecting factors, such as those related to cascade/blade geometry and flow conditions.Also, controlling AVDR is limited by the facility capability.Furthermore, real blade-to-blade flow in turbomachines is usually associated with AVDR greater than unity due to limited span of blades between the hub and shroud such that cascade testing without reducing AVDR could be favored sometimes.All these facets add complexity and diversification to the matter.The current paper reviews previous studies and results on AVDR.Consolidated understanding on the role of AVDR and recommendations on how to deal with it in linear cascade testing are provided.      

基于旋转坐标系转轴振动信号的滚动轴承故障诊断方法

实验验证了基于旋转坐标系转轴振动信号的滚动轴承局部故障诊断方法.建立起滚动轴承故障实验台, 采用压电晶体加速度传感器测取转轴的振动.通过滚动轴承内环、外环、滚动体和保持架四种典型故障的实验分析结果, 证明了该方法的可行性, 并发现转轴振动信号的传递路径相对简单, 对轴承内环、滚动体和保持架早期微小故障的发现与诊断可能是有利的.      

改进的翼型积冰数值模拟方法

基于微团概念,以雨滴运动的基本假设出发,对翼型积冰的拉格朗日方法进行了改进.新方法克服了原方法计算效率低、冰形同雨滴分布密度及网格密度相关的缺点,继承了原方法的鲁棒性,不存在欧拉方法的收敛性问题.为验证方法的正确性,计算了NACA0012翼型在0°和4°攻角下的霜冰积冰情况、表面水收集量和局部撞击系数.结果表明新方法减少了计算量,预测的冰形和实验结果一致,并分析了积冰对气动性能的影响.      

超声马达工作性能影响因素的研究

系统阐述在超声马达的设计、制作过程中的机构设计、制作工艺、驱动电源等方面的关键问题。重点讨论超声振子的粘接技术,振子参数优化设计的方法,以及驱动电源的匹配和频率自动跟踪设计,最终设计出高性能的超声马达。     

应用于控制力矩陀螺电源模块的线性-非线性协同控制器设计

对控制力矩陀螺电源的控制器进行研究,基于对线性控制器及非线性控制器的分析,设计了线性-非线性协同控制器并搭建硬件模型,在电源系统动态调整阶段采用非线性控制器,在稳态阶段采用线性控制器。该方案较单一线性控制器提升了电源系统的动态性能,并保证了系统的稳定性及可靠性。对该鲁棒控制器进行了仿真分析并搭建实物,通过实验验证了相较线性控制器,使用协同控制器的电源模块在控制状态切换时,下冲量与调节时间分别减少了45%及58%,证明了该协同控制器能够有效提升CMG电源系统的动态性能。     

A Novel Onboard-gateway-based Mechanism to Improve TCP Performance in Aeronautical Satellite Networks

The IP-based networks on aircraft serve to support Internet services via satellites. However, in aeronautical satellite hybrid net- works,the TCP protocol performance often deteriorates due to improper decreases and slow recovery of the congestion window. This paper proposes a window size determination and notification mechanism, onboard-gateway-based mechanism (OGBM), which is based on the onboard gateway in the networks on aircraft. A cross-layer approach is adopted by the onboard gateway to obtain the satellite link bandwidth information. And then, by the gateway, through changing the receiver’s advertised window field in ACK packets, TCP sources are notified of the window size of each TCP source calculated on the ground of bandwidth delay product and flow numbers. The mechanism is able to avoid improper changes of TCP window and serve multiple users. Simulation results show that the mechanism with the fairness index close to 1 improves TCP performance in aeronautical satellite networks.     

多重应力下滚动轴承剩余寿命预测

为了实现多重应力下滚动轴承的剩余寿命预测,有效利用不用应力下的退化数据,提出了一种基于加速模型和贝叶斯（Bayesian）理论的滚动轴承剩余寿命预测方法。通过拟合优度检验和威布尔（Weibull）概率图检验法对滚动轴承试验中的数据进行有效性分析。利用switching Kalman filters(SKF)判断滚动轴承各时刻的退化状态。当滚动轴承进入加速退化时,用指数模型拟合轴承退化过程,利用广义线性对数模型表示退化模型参数与应力的关系,根据修正后的轴承实时退化数据利用贝叶斯算法更新模型参数,得到滚动轴承剩余寿命的概率密度函数,从而实现滚动轴承剩余寿命预测。采用XJTU-SY轴承数据集进行验证,预测结果的均方根误差在20 min以内,证明该方法能够有效预测滚动轴承的剩余寿命。     

基于改进GWO-LightGBM的滚动轴承未知故障诊断

针对滚动轴承未知新故障误判影响轴承安全性和检修效率的问题,提出了一种基于改进灰狼算法（GWO）和轻量级梯度提升机（LightGBM）的故障诊断模型,实现已知/未知故障的高精度判别。为避免单一尺度下特征提取的缺失,对滚动轴承振动信号分别提取时域、频域和小波域特征建立多域特征集。设计了带未知新故障判别机制的GWO-LightGBM模型,并构造含有Halton序列和模拟退火策略的GWO实现了模型参数有效优化。实例试验结果表明,模型对已知和未知类故障平均识别率达99.57%,10次随机试验平均识别率分别比单一分类模型逻辑回归（LR）、最近邻分类器（KNN）和支持向量机（SVM）高21.98%、17.00%、9.27%,验证了模型的有效性和优越性,能高准确率地识别出已知或以前从未出现的新故障。