A novel none once per revolution blade tip timing based blade vibration parameters identification method

The vibration caused blade High Cycle Fatigue (HCF) is seriously affects the safety operation of turbomachinery especially for aero-engine. Thus, it is crucial important to identify the blade vibration parameters and then evaluate the dynamic stress amplitude. Blade Tip Timing (BTT) method is one of the promising method to solve these problems. While, it need a high resolution Once Per Revolution (OPR) signal which is difficult to get for the aero-engine. Here, a Coupled Vibration Analysis (CVA) method for identifying blade vibration parameters by a none OPR BTT is proposed. The method assumes that every real blade has its own vibration performance at a given speed. Whereby, it can take any blade as the reference blade, and the other blades using the reference blade as the OPR for vibration displacement calculating and further parameter identifying. The proposed method is validated by numerical model. Also, experimental studies are carried out on a straight blade and a twisted three dimensional blade test rig as well as a large industrial axial compressor respectively. The results show that the proposed method can accurately identify the blade synchronous vibration parameters and quantitatively evaluate the mistuning in bladed disks, which lays a foundation for the reliability improvement of aero-engine.     

基于压缩感知的叶端定时信号参数辨识方法

叶端定时技术作为一种非接触式测量手段,在实际应用中的主要问题之一是测量信号欠采样。压缩感知方法是解决信号欠采样问题的有效手段,但由于求解过程中引入了正则化项,在提高稀疏度时,降低了幅值重构精度,而转子叶片振动幅值参数的准确辨识对于叶片动应力重构具有重要意义。将叶片振动方程设计矩阵与压缩感知字典相结合,在不依赖先验信息的条件下对叶片振动参数进行辨识。首先,根据振动方程设计矩阵形式及关注的最大振动频率,构造压缩感知字典;其次,通过叶端定时信号稀疏表示中的非零元素所在位置,从压缩感知字典中提取对应原子构成设计矩阵进而求得振动参数;接着,叶端定时（BTT）模拟仿真结果表明,所提方法可有效辨识叶片单模态、多模态振动参数;最后,开展旋转叶片振动测试试验,同时利用应变片和叶端定时系统采集振动信号,结果表明,与应变片测量结果相比,提出的方法振动频率辨识相对误差仅为0.14%;对比4个叶片的位移-应变传递比,其中偏离均值的最大百分比仅为2.15%。     

Multi-coset angular sampling-based compressed sensing of blade tip-timing vibration signals under variable speeds

Blade Tip-Timing (BTT) has been regarded as a promising way of on-line blade vibration monitoring. But blind multi-band BTT vibration reconstruction is a big challenge under variable speeds and under-sampling. In order to deal with it, a novel Compressed Sensing (CS) method is proposed based on Multi-Coset Angular Sampling (MCAS) in this paper. First, multi-coset sampling scheme of BTT vibration signals is presented. Then the CS model of BTT vibration signals is derived in order domain. A sufficient condition of the number of BTT sensors is derived for perfect reconstruction and optimal placement of BTT sensors is determined by minimizing the condition number. In the end, numerical simulations are done to validate the proposed method and the performances of four reconstruction algorithms are compared, i.e., Orthogonal Matching Pursuit (OMP), Multiple Signal Classification (MUSIC), Basis Pursuit Denoising (BPDN) and Modified Focal Underdetermined System Solver (MFOCUSS). Influences of the sensor placement, the number of BTT sensors and measurement noises on the reconstruction performances are also testified. The results demonstrate that the proposed method is feasible and the overall performance of the BPDN algorithm is the best among the four algorithms. Also the reconstruction performance decreases with the accelerations of rotating speed.     

Spatial transformation of general sampling-aliasing frequency region for rotating-blade parameter identification with emphasis on single-probe blade tip-timing measurement

Blade-health monitoring is intensely required for turbomachinery because of the high failure risk of rotating blades. Blade-Tip Timing (BTT) is considered as the most promising technique for operational blade-vibration monitoring, which obtains the parameters that characterize the blade condition from recorded signals. However, its application is hindered by severe undersampling and stringent probe layouts. An inappropriate probe layout can make most of the existing methods invalid or inaccurate. Additionally, a general conflict arises between the allowed and required layouts because of arrangement restrictions. For the sake of economy and safety, parameter identification based on fewer probes has been preferred by users. In this work, a spatial-transformation-based method for parameter identification is proposed based on a single-probe BTT measurement. To present the general Sampling-Aliasing Frequency (SAFE) map definition, the traditional time–frequency analysis methods are extended to a time-sampling frequency. Then, a SAFE map is projected onto a parameter space using spatial transformation to extract the slope and intercept parameters, which can be physically interpreted as an engine order and a natural frequency using coordinate transformation. Finally, the effectiveness and robustness of the proposed method are verified by simulations and experiments under uniformly and nonuniformly variable speed conditions.     

基于方差分析的航空发动机风扇叶片外物撞击识别

航空发动机工作过程中风扇外物撞击事件的检测与识别对飞机飞行安全至关重要。通过风扇叶片外物撞击试验平台模拟真实发动机受外物撞击的过程,研究风扇外物撞击规律与检测识别方法。针对航空发动机的机载参数和加装振动参数对风扇外物撞击事件识别难度高与识别准确率低的问题,开展了基于非接触风扇叶片叶尖振动测量的外物撞击检测试验,提出了基于非接触叶尖振动位移方差威布尔分布函数极大似然估计与自动门限检测系统的风扇叶片外物撞击自动门限识别方法,并获取了风扇转子不同转速下外物撞击叶尖振动位移方差识别门限值。选取风扇转子转速为3 000 r/min状态下,直径为16 mm、质量为2.9 g的外物弹体撞击风扇叶片的振动位移数据进行分析,并采用高速摄像系统对该方法识别结果的可靠性进行验证,结果表明：基于非接触叶尖振动位移方差分析法能够准确识别风扇叶片外物撞击事件、撞击叶片编号与撞击叶片数。     

基于统计特征的航空发动机风扇外物撞击检测

为了检测识别航空发动机工作过程中的风扇外物撞击事件,采用非接触叶尖振动测量系统对风扇叶片叶尖振动位移进行实时采集与检测。通过风扇叶片非接触叶尖振动位移数据统计分析,发现叶尖振动位移服从正态分布,并采用Epps-Pulley假设检验证明。设计了基于统计特征的风扇叶片外物撞击叶尖振动位移检测算法,采用该方法获取了风扇转子不同转速下外物撞击叶尖振动位移检测阈值。对风扇转子转速为3000r/min状态下,直径16mm、质量为2.9g的外物弹体撞击风扇叶片的振动位移数据进行分析,并采用高速摄像系统对该方法识别结果的可靠性进行验证。结果表明:基于统计特征的发动机风扇外物撞击检测方法,能够准确识别外物撞击风扇叶片事件及发生撞击的叶片编号。     

TRT透平叶片振动特性的有限元分析

TRT透平叶片在离心力、稳态气流力以及非稳态气流力的共同作用下会产生振动,且易引起共振,从而导致叶片断裂等情况的发生。根据某企业TRT叶片自身结构和工况特点,利用Solidworks软件中建立TRT叶片的三维实体模型,导入Hypermesh软件建立有限元分析模型,应用Ansys软件分析获得TRT叶片的静态、动态振动特性,为TRT叶片的结构设计和提高机组可靠性等方面提供了理论参考。     

基于压缩感知的整体叶盘多模态振动叶尖定时信号重构方法

叶尖定时测量技术是近年来发展的一种非接触式旋转叶片振动监测方法,具有非入侵性的优点,但是该系统测得的振动信号通常是欠采样的。为了能够重构叶尖定时欠采样信号,本文根据压缩感知理论以及叶尖定时采样原理引入了叶尖同步振动信号稀疏模型以及叶尖振动信号重构方法。对整体叶盘有限元模型进行瞬态分析得到叶尖振动信号,使用优化的正交匹配追踪算法以及基追踪对欠采样信号进行重构并和传统方法进行对比。计算结果表明：在信噪比为30dB的噪声环境下,限制频域的正交匹配追踪算法(OMP-RFD)可以准确地识别出叶尖振动信号的主要频率成分。最后使用试验所获得的叶尖振动信号进行重构,验证了OMP-RFD算法有效性。综上可知：压缩感知方法可以很好地应用于叶尖定时测量装置中,能够使用较少传感器识别叶尖同步振动欠采样信号参数,有效提高噪声环境下识别高阶频率的成功率以及准确度。     

叶片振动应力的叶根振动监测法及实现技术

提出一种航空螺旋桨叶片振动应力监测方法—叶根振动监测法,通过监测螺旋桨振动来获得叶根振动激励,建立叶片振动计算传递矩阵模型,并由叶根激励振动计算叶片的振动响应和振动应力,进而实现对叶片振动应力的监测.叶根振动可以通过直接测量或由螺旋桨转子支承机匣监测振动换算获得.最后,通过对比桨叶振动应力的计算结果与飞行试验实测结果验证该方法.结果表明,叶根振动监测法简便、易行,可以进一步应用.      

多目标约束的精锻叶片几何重构优化算法

新型航空发动机已部分采用精密锻造工艺提高叶片的制造精度和效率。然而,精锻后的叶身型面与设计模型存在几何偏差,导致依据设计模型加工后的进/排气边与精锻的叶身型面不能很好地匹配。为了解决该问题,提出一种面向精锻叶片自适应加工的几何重构方法。首先,建立基于公差约束配准的目标函数,并采用粒子群优化（PSO）算法求解目标函数。其次,提出基于叶身变形趋势预测进/排气边轮廓的光顺重构算法。最后,通过精锻叶片自适应数控加工实验对提出的方案和算法进行验证。实验结果表明,该方案可以有效重构出合格的工艺模型,满足精锻叶片的精密数控加工要求。     

航空发动机叶片前后缘自由式砂带抛光技术

由于叶片前后缘(LTE)的轮廓形状和表面质量将对航空发动机的气动性能和叶片的疲劳性能产生直接影响,因此为提高前后缘的轮廓度和表面质量,通过对目前航空发动机叶片前后缘抛光所存在的问题进行分析,结合叶片前后缘抛光工艺要求,并基于自由式砂带抛光的工艺特点,提出了叶片前后缘自由式砂带抛光工艺方法;针对该抛光工艺方法,建立其砂带张紧力控制系统,确定了抛光加工中的砂带走刀步长计算公式及抛光轨迹规划方法;最后以某型号叶片的前后缘作为加工对象进行抛光实验研究。检测结果显示:叶片前后缘轮廓度误差小于0.01mm,其表面粗糙度小于0.4μm,证实了该抛光工艺方法对提高叶片前后缘的轮廓度和表面质量的有效性。     

Reliability analysis of aero-engine blades considering nonlinear strength degeneration

To comprehensively consider the effects of strength degeneration and failure correlation, an improved stress-strength interference (SSI) model is proposed to analyze the reliability of aeroengine blades with the fatigue failure mode. Two types of TC4 alloy experiments are conducted for the study on the damage accumulation law. All the parameters in the nonlinear damage model are obtained by the tension-compression fatigue tests, and the accuracy of the nonlinear damage model is verified by the damage tests. The strength degeneration model is put forward on the basis of the Chaboche nonlinear damage theory and the Griffith fracture criterion, and determined by measuring the fatigue toughness during the tests. From the comparison of two kinds of degeneration models based on the Miner’s linear law and the nonlinear damage model respectively, the nonlinear model has a significant advantage on prediction accuracy especially in the later period of life. A time-dependent SSI reliability model is established. By computing the stress distribution using the finite element (FE) technique, the reliability of a single blade during the whole service life is obtained. Considering the failure correlation of components, a modified reliability model of aero-engine blades with common cause failure (CCF) is presented. It shows a closer and more reasonable process with the actual working condition. The improved reliability model is illustrated to be applied to aero-engine blades well, and the approach purposed in this paper is suitable for any actual machinery component of aero-engine rotor systems.     

民用航空发动机振动监测方法研究

对民用航空发动机振动监测原理、监测算法和发动机振动位移量到飞机振动单位的转换关系进行了系统研究。并在此基础上,结合民用航空发动机典型的叶片磨损问题,分析了发动机振动值随不同叶片磨损程度的变化趋势;给出了发动机振动检查程序,便于在飞机地面维护和飞行过程中提早发现发动机存在的结构故障,保障飞行安全。本文结合发动机振动特点提出的工程检测方法,具有一定的工程使用价值和参考意义。     

叶尖定时技术在转子叶片故障排除中的应用

介绍了基于叶尖定时的非接触振动测试系统的基本原理和数据分析方法,并将之应用于某型航空发动机压气机第5级转子叶片掉块故障分析。通过优化传感器的轴向分布和周向布局,分别测量3类叶片（原型、优化和削角）前缘和尾缘位置高阶振动信息,包括共振转速、频率和幅值。分别对比原型、优化叶片的前缘和尾缘测试结果,每类叶片的前缘和尾缘位置的共振幅值基本一致;对比3类叶片的前缘位置振动参数,优化叶片的共振转速较原型叶片有所增加,削角叶片的共振转速较优化叶片有所增加;对比原型、优化叶片尾缘位置振动参数,优化叶片的共振转速较原型叶片有所增加。可以判断:原型叶片发生疲劳断裂主要原因是其在工作转速4200 r/min出现高阶模态共振现象;优化叶片的共振转速较原型叶片增加40～80 r/min,叶片故障率降低,而削角叶片的共振转速较原型叶片增加140～180 r/min,且对应尾缘位置削角,可以排除叶片掉块故障。      

稳态温度场作用下涡轮叶片振动特性的研究

根据一维线性分段插值方法对一维温度场进行分段插值拟合,为有限元方法分析温度场作用下叶片振动问题中温度载荷的施加提供了简化可行的方案,利用有限元方法验证该线性拟合方案的可行性,并采用多种方法对计算结果进行验证分析,对一维稳态温度场的分段插值进行了误差分析,得到稳态温度场作用下叶片振动问题的有限元分析计算方法和温度载荷施加方案。     

航空发动机转子叶片的声振疲劳特性试验

由于振动载荷、声载荷等会造成航空发动机转子叶片的高周疲劳失效,通过试验研究了发动机转子叶片的声振疲劳特性.首先研究了叶根边界条件对发动机转子叶片声响应特性的影响,对3组叶片施加不同的顶紧力,由低到高施加声载荷,试验得到不同叶根边界条件下叶片的声响应与声载荷的关系,并且得出叶根边界条件对转子叶片声响应大小有影响的结论.随后研究了叶片的声振疲劳特性,得出以下结论:声载荷作用下,叶片确实可以发生破坏.在两种不同的激励方式（随机振动激励与正弦振动激励）作用下叶片的频率下降略有不同,并且在动应力水平相同的条件下,发动机转子叶片在正弦振动激励下的寿命远大于其在随机振动激励下的寿命.      

涡轮叶片早期裂纹的三维叶尖间隙EEMD能量熵融合诊断方法

为了解决航空发动机涡轮叶片早期裂纹故障信号微弱、难以识别的问题,提出一种基于三维叶尖间隙集成经验模态分解（EEMD）能量熵融合的涡轮叶片早期裂纹诊断方法。采集涡轮叶片三维叶尖间隙信息,利用EEMD分别对三维叶尖间隙各维信号进行处理,得到相应的固有模态函数(IMF),以此计算每一维信号分量EEMD能量熵,构建能表征叶片裂纹状态的不同EEMD能量熵高维矢量集。建立多个堆叠自动编码器(SAE)分别对各高维矢量集进行特征学习并提取所学习的深层特征表达。利用支持向量机算法(SVM)和遗传算法(GA)融合各维深层特征以综合不同维度信息进而充分判定叶片裂纹状态。通过涡轮叶片裂纹诊断试验,结果表明:所提方法能有效提高叶片早期裂纹诊断精度,其平均准确率达到98.415%,标准差仅为0.697%,具有很好的稳定性、泛化性和自适应性。     

基于双判定准则的航空发动机叶片缺陷孔洞的边界提取

航空发动机叶片缺陷孔洞的几何形状复杂、位置分布散乱且曲率变化明显,使用单一判定准则难以完整地提取其边界特征点.为实现叶片缺陷孔洞边界的精准提取和孔洞边界线的平滑拟合,提出了一种基于双判定准则的航空发动机叶片缺陷孔洞边界特征点的提取算法.该算法采用K–Dimension tree(K–D树)建立点云的空间拓扑结构,以采样...     

Neumann 随机有限元法计算涡轮叶片的可靠度

推导了具有随机性时Ｎｅｕｍａｎｎ随机有限元法的基本方程,用级数来拟合结构响应的概率密度函数,并通过数值积分计算结构的可靠度。对涡轮叶片的可靠度分析表明,该方法可利用较小的样本容量获得较高的计算精度,并具有较高的计算效率。     

某型钛铝合金航空发动机叶片高温高周振动疲劳实验

以某型钛铝合金航空发动机叶片为研究对象,针对该型叶片高温高周振动疲劳实验时遇到的高温疲劳应力监测、高频激励等问题进行了实验方法研究。采用闭环控制最大应力的方法解决了高温疲劳应力的监测,通过夹具放大设计实现了高频激励,利用辐射加热和电磁振动台完成了温度载荷和振动载荷的综合施加。运用所述的高温高周振动疲劳实验方法,对该型叶片进行了寿命实验。实验的高温疲劳应力控制精度优于±2%,得到该型叶片可靠度为50%的中值疲劳极限是444 MPa,并有效获得了其寿命曲线。该实验方法适合航空发动机叶片高温高周振动疲劳实验,并可为其他航空发动机零部件高温高周疲劳实验提供参考。