高可靠小条件数压缩感知叶尖定时信号辨识

为增强基于任意角度压缩感知(CS)叶尖定时信号(BTT)重构的稳定性,采用小生境微种群遗传算法提出了基于小条件数的高稳定性传感器安装布置方法。研究发现:任意角度CS的恢复矩阵条件数越小,不同信噪比下同步和非同步振动信号重构误差越趋于稳定在较低范围,优化后发现条件数为1的排布方案下,相邻传感器角度间隔趋向于均布2m取K的形式。利用条件数为1的矩阵冗余特性,设计了高可靠性的传感器排布方案,数值试验和有限元仿真验证表明叶尖定时系统在失效一个传感器的情况下仍可稳定得到准确的重构结果。在信噪比为5 dB时,冗余排布相比参考排布的主倍频幅值重构误差降低4.4%以上,验证了采用最小条件数排布的任意角度CS信号辨识方法在噪声及传感器失效情况下仍可保证BTT测量结果的有效性和可靠性。      

Impulse feature extraction method for machinery fault detection using fusion sparse coding and online dictionary learning

Impulse components in vibration signals are important fault features of complex machines. Sparse coding(SC) algorithm has been introduced as an impulse feature extraction method, but it could not guarantee a satisfactory performance in processing vibration signals with heavy background noises. In this paper, a method based on fusion sparse coding(FSC) and online dictionary learning is proposed to extract impulses efficiently. Firstly, fusion scheme of different sparse coding algorithms is presented to ensure higher reconstruction accuracy. Then, an improved online dictionary learning method using FSC scheme is established to obtain redundant dictionary and it can capture specific features of training samples and reconstruct the sparse approximation of vibration signals. Simulation shows that this method has a good performance in solving sparse coefficients and training redundant dictionary compared with other methods. Lastly, the proposed method is further applied to processing aircraft engine rotor vibration signals. Compared with other feature extraction approaches, our method can extract impulse features accurately and efficiently from heavy noisy vibration signal, which has significant supports for machinery fault detection and diagnosis.     

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.     

一种基于最小流形长度的高精度线阵设计

 传统上以阵列孔径来比较各种线阵的测向性能,但这一指标没有考虑到阵元数目及阵列几何布局对阵列性能的影响。因子空间类算法需搜索阵列流形来进行测向,所以阵列流形对阵列性能有重要影响。阵列流形又是阵元数、阵列几何布局的函数,可通过阵列流形来研究阵元数、阵列几何布局与阵列测向性能的关系。在分析了线阵流形与线阵测向精度的关系的基础上,提出了一个基于线阵最小流形长度的指标来比较不同线阵的性能,该指标综合考虑了阵元数、阵列几何布局对阵列测向精度的影响。通过对此指标的讨论,给出了提高线阵测向精度的设计思路。对几个不同线阵的仿真证实了该指标的有效性,也验证了该设计思路的可行性。     

Multi-probe linear fitting and time of arrival linear correction method to analyze blade vibration based on blade tip timing without once-per-revolution

Blade vibration monitoring can ensure the safe operation of aero-engine rotor blades. Among the methods of blade vibration monitoring, Blade Tip Timing (BTT) method has attracted more and more attention because of its advantages of non-contact measurement. However, it is difficult to install the Once-Per-Revolution (OPR) probe in the confined space of aero-engine, and the failure and instability of the OPR signal will reduce the reliability of the blade vibration analysis results, which directly affects the accuracy of the blade vibration parameters identification. The Multi-Probe linear fitting and Time of Arrival (ToA) Linear Correction method based on the BTT (MP-LC-BTT) without OPR is proposed to reduce the errors of single probe linear fitting method for blade vibration displacement analysis. The proposed method can also correct the calculation error of blade vibration displacement due to the nonlinear change of rotation speed, which can improve the analysis accuracy of the blade vibration displacement. A new blade vibration model conforming to the actual vibration characteristics is established, and the effectiveness of the proposed method is verified by numerical simulation. Finally, the reliability and accuracy of the MP-LC-BTT method have been verified by the experiments which include two high-speed blade test-benches and an industrial axial fan. This method can be used in the actual aero-engine monitoring instead of the BTT method with OPR.     

基于振动传递符号有向图的齿轮箱嵌入式传感器优化配置模型与算法

针对嵌入式条件下振动信号的采集方式、传递路径与传统方式不同的特点,采用有限元仿真方法,分析了嵌入式传感下齿轮箱故障振动的传递特性,在此基础上建立了基于故障振动传递符号有向图的优化配置模型,并研究了基于粒子群优化的求解算法.案例仿真结果验证了上述方法的有效性,为在齿轮箱内部合理嵌入传感器提供了一种可行途径.      

基于任意传感器排布的叶尖定时信号压缩感知辨识方法

基于叶片振动和叶尖计时系统的特点,推导了任意传感器角度分布下叶片振动响应的分布规律,并基于叶尖定时信号频域的稀疏性,探究了压缩感知方法在叶尖定时信号的重构和倍频辨识上的应用。通过同步信号和非同步信号的大量数值实验,分析了包括信号重构误差、传感器数量、旋转周期数、频率分辨率和信噪比等因素对辨识效果的影响,提出了压缩感知重构叶尖定时信号的基本方法和步骤。进一步将压缩感知方法应用于某型号涡扇发动机钛合金宽弦风扇叶片有限元仿真获得的振动响应数据重构,验证了该方法的有效性。结果表明:在加入了叶尖测点位置误差和30 dB随机噪声的前提下,在80%、90%和100%三种转速工况下均清晰辨识出幅值较大的激励倍频。基于任意传感器角度分布的压缩感知方法对叶尖定时信号辨识效果较好。     

有输入未建模动态的导弹鲁棒控制器设计

在导弹系统俯仰通道中存在输入未建模动态情况下，提出了一种基于RBF神经网络和反演控制技术的非线性鲁棒控制器的设计方法。首先应用两个RBF神经网络对输入未建模动态设计了神经网络逆补偿器．然后利用反演控制技术设计了导弹非线性控制器．最后应用Lyapunov稳定性理论推导出RBF神经网络权重矢量调节律，证明了系统的所有信号均有界且全局指数收敛至原点。最后给出的BTT导弹非线性六自由度数字仿真结果显示了该设计方法的有效性。     

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。     

风扇/压气机周向模态识别中传感器角度偏差的影响

基于任意角度压缩感知(CS)方法分析了传感器安装角度偏差对风扇/压气机周向模态识别重构的影响,设计了一套自适应角度优化程序修正重构误差。利用数值试验探究了传感器角度偏差和数量对周向模态重构结果的影响,研究表明:当角度偏差等级为2.5%时,平均重构误差达到10%以上,若保证重构误差基本不变,将传感器数量从7个增加至25个,仅可以将角度偏差等级放宽至4%。而采用小生境微种群遗传算法进行自适应角度优化,在20 dB信噪比下,通过自适应角度优化可将角度偏差等级从2.5%放宽至10%,降低了传感器安装的精度要求。成功优化了一款冷却风扇在前三阶叶片通过频率下的主要周向声模态重构幅值。自适应角度优化算法有效提升了基于压缩感知的风扇/压气机周向模态重构可靠性。     

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%。     

BTT导弹的神经网络自适应反馈线性化控制

提出基于反馈线性化理论的BTT导弹自动驾驶仪设计方法。并提出由俯仰过载命令产生攻角命令的方法。应用CMAC神经网络构成一种自适应反馈线性化方案,使具有不确定性的实际系统获得要求的跟踪性能。闭环系统的稳定性由李雅普诺夫稳定性理论得到保证。该方法适合于控制导弹作全空域飞行。     

嵌套阵列最大似然估计测向算法

针对在辐射源个数未知的条件下嵌套阵列难以估计多个辐射源角度的问题,提出了基于最大似然估计(MLE)的嵌套阵列角度估计算法。算法在嵌套阵列模型的基础上,首先通过推导阵列截获多辐射源信号的最大似然函数及其梯度,利用最速下降法估计出空域中所有潜在辐射源的角度;然后,通过多元假设检验,利用最大似然比与门限进行比较,确定出空域中所有潜在辐射源中某一时刻发射信号的活跃辐射源角度,排除其余噪声形成的虚假辐射源角度,解决了在辐射源个数未知条件下嵌套阵列对多个辐射源角度估计问题。仿真结果表明:与传统多重信号分类(MUSIC)算法相比,该算法在辐射源数目未知、存在相干信号、低信噪比(SNR)、低快拍数条件下,均具有较好的角度估计精度,并且算法形成的虚拟阵列自由度是空间平滑MUSIC算法的2倍;多元假设检验法比传统信源数目估计算法在低信噪比条件下和处理相干信号方面具有明显优势。     

带有广义不确定性的导弹非线性控制系统设计

 在导弹系统存在广义不确定性情况下,提出了一种基于反演设计技术和RBF神经网络的导弹非线性自适应控制系统设计方法。设计过程中将不确定性对系统的影响合成为一项,然后应用RBF神经网络消除了其对系统的影响,有效地解决了控制系数矩阵未知时控制器设计问题,同时放松了现有文献中对控制系数矩阵不确定性的要求。应用Lypunov稳定性定理推导出神经网络权重矩阵的自适应调节律,并证明了闭环系统的所有信号均有界且指数收敛至系统原点的一个邻域。最后给出的BTT导弹非线性六自由度数字仿真结果验证了该算法的有效性。     

多模型切换控制及其在BTT导弹设计中的应用

研究了一类可以抑制切换时刻输出跳跃的多模型切换控制器的设计问题。基于参数化特征结构配置结果及模型跟踪方法,设计了满足输出跟踪性能并使多模型系统中各闭环子系统渐近稳定的多模型切换控制律集合。充分利用参数化特征结构配置方法提供的全部自由度,协调选取各子系统控制律中的参数来抑制切换时刻的输出跳跃。将本文提出的方法应用到某型倾斜转弯导弹自动驾驶仪设计中,仿真结果表明导弹的输出过载迅速准确跟踪制导指令,并且切换抖动得到有效抑制。     

Reconstructed Flight Control Sensor Signals Via Luenberger Observers

The feasibility of providing flight control systems with software-managed redundancy is investigated. It is shown that sufficient information can be extracted from aircraft attitude sensors to allow analytic reconstruction of the stability augmentation system (SAS) sensor signals in the event of SAS sensor failures. The sensor signal reconstruction is performed in a deterministic setting by using a Luenberger observer. A simple and efficient design procedure is presented based on Gopinath's [1] work. As an illustrative example, the proposed sensor signal reconstruction technique is exercised on the L-1011 aircraft lateral axis rigid body simulation. It is shown that with an accurate airframe model perfect reconstruction is feasible. In addition, it is also shown via simulation results that with imperfect knowledge of the airframe model (based on standard wind tunnel data), the proposed reconstruction is accurate enough to have a negligible impact on the overall aircraft performance.     

基于声波传播时间估计的气流速度测量方法

 为实现对亚声速和超声速气流速度的统一测量,提出了一种基于声传感器的新型测量方法。首先,根据声波在亚声速和超声速气流中的传播特性,利用特定的测量装置建立了声波传播时间与气流速度之间的数学模型,从而将气流速度的测量问题转化为声波传播时间的测量问题。然后,在此基础上,利用计时法和最大似然估计(MLE)方法来估计声波传播时间;其中,计时法在实时性上优势明显,而MLE方法则在可靠性上优于前者。最后,分别从阵元位置扰动性、计时误差和克拉美-罗界(CRB)3个方面对所提算法的性能进行了分析与仿真验证。结果表明,该算法能够实现对亚声速和超声速气流速度的精确测量。     

Synchronous chirp mode extraction: A promising tool for fault diagnosis of rolling element bearings under varying speed conditions

As critical components in modern aerospace productions, rolling element bearings(REBs) generally work under varying speed conditions, which brings great challenges to their operating health monitoring. Some novel time–frequency decomposition(TFD) algorithms are established recently to extract nonlinear features from the non-stationary signals effectively, which are promising for realizing fault diagnosis of REBs under varying speed conditions. However, numerous personal experiences must be incor...     

ROBUST ADAPTIVE BANK-TO-TURN MISSILE AUTOPILOT DESIGN USING FUZZY CMAC NEURAL NETWORKS

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BTT导弹的大系统变结构控制方法

将BTT导弹控制系统表示成具有非匹配不确定性大系统的形式，利用反演设计方法处理系统中的非匹配不确定性．用变结构控制方法来改善系统的性能，然后，利用Lyapunov稳定性定理证明了闭环系统的信号最终一致有界且系统的状态指数收敛于滑模超平面。最后．仿真验证了所提出方法的正确性和有效性。