Remaining useful life prognostics for aeroengine based on superstatistics and information fusion

Remaining useful life（RUL） prognostics is a fundamental premise to perform conditionbased maintenance（CBM） for a system subject to performance degradation. Over the past decades,research has been conducted in RUL prognostics for aeroengine. However, most of the prognostics technologies and methods simply base on single parameter, making it hard to demonstrate the specific characteristics of its degradation. To solve such problems, this paper proposes a novel approach to predict RUL by means of superstatistics and information fusion. The performance degradation evolution of the engine is modeled by fusing multiple monitoring parameters, which manifest non-stationary characteristics while degrading. With the obtained degradation curve,prognostics model can be established by state-space method, and then RUL can be estimated when the time-varying parameters of the model are predicted and updated through Kalman filtering algorithm. By this method, the non-stationary degradation of each parameter is represented, and multiple monitoring parameters are incorporated, both contributing to the final prognostics. A case study shows that this approach enables satisfactory prediction evolution and achieves a markedly better prognosis of RUL.     

H∞ output tracking control for flight control systems with time-varying delay

For flight control systems with time-varying delay, an H∞ output tracking controller is proposed. The controller is designed for the discrete-time state-space model of general aircraft to reduce the effects of uncertainties of the mathematical model, external disturbances, and bounded time-varying delay. It is assumed that the feedback-control loop is closed by the communication network, and the network-based control architecture induces time-delays in the feedback information. Suppose that the time delay has both an upper bound and a lower bound. By using the Lyapu- nov-Krasovskii function and the linear matrix inequality （LMI）, the delay-dependent stability criterion is derived for the time-delay system. Based on the criterion, a state-feedback H∞ output tracking controller for systems with norm-bounded uncertainties and time-varying delay is presented. The control scheme is applied to the high incidence research model （HIRM）, which shows the effectiveness of the proposed approach.     

Orbital reference frame estimation with power spectral density constraints for drag-free satellites

The drag-free satellites are widely used in the field of fundamental science as they enable the high-precision measurement in pure gravity fields. This paper investigates the estimation of local orbital reference frame (LORF) for drag-free satellites. An approach, taking account of the combi-nation of the minimum estimation error and power spectral density (PSD) constraint in frequency domain, is proposed. Firstly, the relationship between eigenvalues of estimator and transfer func-tion is built to analyze the suppression and amplification effect on input signals and obtain the eigenvalue range. Secondly, an optimization model for state estimator design with minimum estima-tion error in time domain and PSD constraint in frequency domain is established. It is solved by the sequential quadratic programming (SQP) algorithm. Finally, the orbital reference frame estimation of low-earth-orbit satellite is taken as an example, and the estimator of minimum variance with PSD constraint is designed and analyzed using the method proposed in this paper.     

Probability hypothesis density filter with adaptive parameter estimation for tracking multiple maneuvering targets

The probability hypothesis density (PHD) filter has been recognized as a promising tech-nique for tracking an unknown number of targets. The performance of the PHD filter, however, is sensitive to the available knowledge on model parameters such as the measurement noise variance and those associated with the changes in the maneuvering target trajectories. If these parameters are unknown in advance, the tracking performance may degrade greatly. To address this aspect, this paper proposes to incorporate the adaptive parameter estimation (APE) method in the PHD filter so that the model parameters, which may be static and/or time-varying, can be estimated jointly with target states. The resulting APE-PHD algorithm is implemented using the particle filter (PF), which leads to the PF-APE-PHD filter. Simulations show that the newly proposed algorithm can correctly identify the unknown measurement noise variances, and it is capable of tracking mul-tiple maneuvering targets with abrupt changing parameters in a more robust manner, compared to the multi-model approaches.     

A step deformation method for superplasticity improvement of coarse-grained Ti–15V–3Cr-3Sn-3Al

Ti–15V–3Cr–3Sn–3Al(Ti–15–3), a kind of metastable beta titanium which has high specific strength and good cold-formability, is highlighted for applications in the aerospace manufacture industry. However, the technique for improving its formability at elevated temperatures is still a challenge at present. In this work, a step deformation method is proposed for superplasticity improvement of coarse grained Ti–15–3 plates at temperatures around its beta transus. The effects of the strain rate and the strain at the first stage on the superplasticity are investigated. The results show an increase of the strain rate sensitivity and a decrease of the flow stress under the step deformation mode compared to those obtained under constant strain rates at 780℃. The maximum strain to failure obtained in the step mode is 93% higher than that deformed in the constant strain rate mode. Strain rates, strains at the first stage, and temperatures have influences on the superplasticity improvement. The deformation mechanism is concluded as subgrain formation accommodated by grain boundary sliding rate-controlled by dislocation climb. The improved m value in the step deformation is accounted to the extra dislocation density produced during the strain rate reduction.     

Investigation of lateral-directional aerodynamic parameters identification method for fly-by-wire passenger airliners

A new identification method is proposed to solve the problem of the influence on the loaded excitation signals brought by high feedback gain augmentation in lateral-directional aerodynamic parameters identification of fly-by-wire（FBW） passenger airliners. Taking for example an FBW passenger airliner model with directional relaxed-static-stability, through analysis of its signal energy distribution and airframe frequency response, a new method is proposed for signal type selection, signal parameters design, and the appropriate frequency relationship between the aileron and rudder excitation signals. A simulation validation is presented of the FBW passenger airliner＇s lateral-directional aerodynamic parameters identification. The validation result demonstrates that the designed signal can excite the lateral-directional motion mode of the FBW passenger airliner adequately and persistently. Meanwhile, the relative errors of aerodynamic parameters are less than 5%.     

Hypervelocity impact induced shock acoustic emission waves for quantitative damage evaluation using in situ miniaturized piezoelectric sensor network

Manmade debris and natural meteoroids, travelling in the Low Earth Orbit at a speed of several kilometers per second, pose a severe safety concern to the spacecraft in service through the HyperVelocity Impact(HVI). To address this issue, an investigation of shock Acoustic Emission(AE) waves induced by HVI to a downscaled two-layer Whipple shielding structure is performed,to realize a quantitative damage evaluation. Firstly a hybrid numerical model integrating smoothparticle hydrodynamics and finite element is built to obtain the wave response. The projectiles, with various impact velocities and directions, are modelled to impact the shielding structure with different thicknesses. Then experimental validation is carried out with built-in miniaturized piezoelectric sensors to in situ sense the HVI-induced AE waves. A quantitative agreement is obtained between numerical and experimental results, demonstrating the correctness of the hybrid model and facilitating the explanation of obtained AE signals in experiment. Based on the understanding of HVI-induced wave components, assessment of the damage severity, i.e., whether the outer shielding layer is perforated or not, is performed using the energy ratio between the regions of ‘‘high frequency" and ‘‘low frequency" in the acquired AE signals. Lastly, the direct-arrival fundamentalsymmetric wave mode is isolated from each sensing signal to be input into an enhanced delay-andsum algorithm, which visualizes HVI spots accurately and instantaneously with different sensor network configuration. All these works demonstrate the potential of quantitative, in situ, and real time HVI monitoring using miniaturized piezoelectric sensor network.     

Dynamics modeling and control of a transport aircraft for ultra-low altitude airdrop

The nonlinear aircraft model with heavy cargo moving inside is derived by using the separation body method, which can describe the influence of the moving cargo on the aircraft attitude and altitude accurately. Furthermore, the nonlinear system is decoupled and linearized through the input–output feedback linearization method. On this basis, an iterative quasi-sliding mode(SM)flight controller for speed and pitch angle control is proposed. At the first-level SM, a global dynamic switching function is introduced thus eliminating the reaching phase of the sliding motion.At the second-level SM, a nonlinear function with the property of ‘‘smaller errors correspond to bigger gains and bigger errors correspond to saturated gains' ' is designed to form an integral sliding manifold, and the overcompensation of the integral term to big errors is weakened. Lyapunovbased analysis shows that the controller with strong robustness can reject both constant and time-varying model uncertainties. The performance of the proposed control strategy is verified in a maximum load airdrop mission.     

Real-time fault detection method based on belief rule base for aircraft navigation system

Real-time and accurate fault detection is essential to enhance the aircraft navigation system’s reliability and safety. The existent detection methods based on analytical model draws back at simultaneously detecting gradual and sudden faults. On account of this reason, we propose an online detection solution based on non-analytical model. In this article, the navigation system fault detection model is established based on belief rule base (BRB), where the system measuring residual and its changing rate are used as the inputs of BRB model and the fault detection function as the output. To overcome the drawbacks of current parameter optimization algorithms for BRB and achieve online update, a parameter recursive estimation algorithm is presented for online BRB detection model based on expectation maximization (EM) algorithm. Furthermore, the proposed method is verified by navigation experiment. Experimental results show that the proposed method is able to effectively realize online parameter evaluation in navigation system fault detection model. The output of the detection model can track the fault state very well, and the faults can be diagnosed in real time and accurately. In addition, the detection ability, especially in the probability of false detection, is superior to offline optimization method, and thus the system reliability has great improvement.     

环境激励下基于chirplet变换的线性时变系统参数识别

提出了针对时变系统响应的短时频率线性时变假设,通过将时变响应拟合成多分量线调频信号,根据线调频信号互相关理论推导了随机白噪声激励下时变系统的物理参数识别方法。该识别方法只需基于结构的加速度响应,便能识别结构的时变质量和刚度。由于引入了调频斜率刻画响应信号的短时频率线变特征,该方法相比传统识别方法能更好地追踪快变甚至突变参数,对实际工程中的时变问题具有重要的应用价值。仿真算例中构造了1个3自由度时变结构模型,针对线性时变、周期时变和突变等情况进行了物理参数的识别,误差分析显示识别误差均在5%以内,仿真结果验证了方法的正确性和适用性。     

基于经验模态分解的燃烧不稳定性分析

实验研究了在常温常压条件下贫燃预混旋流火焰的燃烧不稳定性,发生燃烧不稳定性时其压力脉动表现为非平稳信号.利用一种基于经验模态分解(EMD)的希尔伯特-黄变换(HHT)算法对在当量比分别为0.71和0.80工况下的压力脉动信号进行了时频分析.针对压力脉动信号进行经验模态分解,选取主要的固有模态函数(IMF),对IMF通过HHT变换得到瞬时频率并进行统计分析.结果表明:在当量比为0.71时,压力信号呈间歇式的脉动,其振型为拍振;在当量比为0.80时,脉动压力信号则呈现出极限环振型.在基于EMD的HHT变换中,IMF体现了燃烧不稳定性的固有模态且具有自适应性强的特点.      

基于Kriging形函数的线性时变结构模态参数辨识

近年来,对航空航天飞行器随时间变化的动力学特性研究需求越来越迫切。仅输出参数化时域的时变时间序列模型以其结构简约、精度高且跟踪能力强而成为研究热点,尤其是泛函向量时变自回归(FS-VTAR)模型已经得到了广泛应用。然而传统的FS-VTAR模型在保证其辨识优势的同时却需要针对不同时变结构选择合适的基函数形式及较高的基函数阶数,该过程相当复杂且耗时。本文借鉴无网格法中移动最小二乘(MLS)法构造形函数的思想,提出一种基于Kriging形函数的线性时变结构模态参数辨识方法。该方法首先引入自适应于辨识信号的Kriging形函数;再把时变系数在形函数上线性展开,利用最小二乘(LS)法得到形函数的展开系数;最后把时变模型特征方程转换为广义特征值问题提取出模态参数。利用时变刚度系统非平稳振动信号验证该方法,结果表明:基于Kriging形函数的FS-VTAR模型相比于传统的FS-VTAR模型能有效地避免基函数形式的选择和较高的基函数阶数,且精度相当;相比于移动最小二乘法能有效地解决其数值条件问题且具有更高的模态参数辨识精度。     

基于时频切片分解的时变系统参数识别

系统参数识别分为时不变系统参数识别和时变系统参数识别两大研究方向,其中时不变系统参数识 别的研究已趋于成熟,而时变系统参数识别的研究则仍然处于起步阶段。对于多自由度时变结构,提出一种基 于时频切片分解的时变系统参数识别方法。该方法采集结构的振动位移响应,根据时频分解计算得到响应在 整个时频段内的时频能量分布图;依据结构的时频分布特性,选择多个时频切片窗分解响应信号,再对分解出 的信号分别进行逆变换计算完成时域上的信号重构;重构出来的信号对应于结构的各阶模态位移响应信号,利 用Hilbert变换提取信号瞬时频率,从而识别出结构各阶频率。通过一个三自由度的弹簧阻尼质量仿真实验, 验证了该方法具有良好的识别精度和工程实用价值。     

基于自适应分段混合系统的轴承故障诊断

针对强噪声背景下轴承早期故障的诊断问题,提出一种基于自适应分段混合随机共振（adaptive piecewise hybrid stochastic resonance,APHSR）系统的检测方法。采用经验模态分解法（EMD）进行信号预处理,分别采用能量密度法和相关系数法去除高、低频噪声,自动筛选最优固有模态函数,经尺度变换后输入分段混合随机共振系统模型,提取故障信号。工程实验显示:经过APHSR系统,轴承故障特征频率的频谱幅值、频谱幅值与周围最大噪声之差和最大信噪比（SNR）均高于经验模态分解和经典随机共振方法,其中齿轮箱故障轴承信噪比分别提高了9.579 dB和7.473 dB,转子故障轴承信噪比分别提升了8.597 dB和5.695 dB,对凯斯西储大学故障轴承数据处理后的信噪比分别提升了3.369 dB和17.043 dB。数据表明APHSR方法具有高效性,提高了轴承故障信号诊断能力。      

基于EMD的发动机轴承振动冲击故障特征提取

采用经验模式分解方法(EMD),研究了发动机轴承的非平稳振动信号故障特征提取问题.计算机仿真结果证实了该方法的有效性;采用该方法提取了滚动轴承故障振动信号冲击特征,结果表明应用该方法能够准确、有效地获得轴承的冲击损伤特征,并且,经进一步分析,可确定冲击损伤故障失效模式.     

基于经验模态分解的航空器结构模态参数识别

介绍了一种基于经验模式分解（Empirical Mode Decomposition,EMD）与粒子群优化算法相结合的飞机结构模态参数辨识方法。一个复杂的脉冲响应信号利用EMD方法使得耦合在一起的多阶模态响应信号分解为与各单阶模态响应信号一一对应的分量,得到前几阶主要的内禀模式函数（Intrinsic Mode Function,IMF）,再对分解得到的每一单阶IMF利用粒子群算法辨识得到各阶模态参数。试验仿真结果表明该方法有较高的计算精度,可应用于结构运行模态分析,为飞机等结构设计、运行检测提供有力保障。     

高速飞行器热结构工作时变模态参数辨识

高速飞行器由于其很高的飞行速度而无可避免地受到气动加热作用的影响,进而引起结构特性的时变。采用理论或有限元方法(FEM)进行数值分析,难以获取反映结构在飞行(工作)状态下的真实模态参数。通过辨识获取高速飞行器热环境下的时变结构模态参数是一项十分具有挑战性的任务。针对此问题,引入参数化时频域的最大似然方法,对气动加热作用下的高速飞行器升力面结构的时变模态参数进行了辨识。通过模拟真实飞行状态的数值算例研究,说明参数化时频域的最大似然方法能够很好地辨识出低信噪比(SNR)情况下的模态频率和模态振型,验证了参数化时频域最大似然方法适用于具有显著时变特征的高速飞行器热结构的时变结构模态参数辨识,可为将来相关的工程研究和应用提供良好的理论支持。     

减速器振动信号的经验模态分解

时域同步平均是直升机减速器诊断技术的基础,目前这种方法依赖于转速传感器提供相位同步信号。探讨了应用经验模态分解代替时域同步平均分析减速器振动信号的方法。构建了一个减速器振动信号模型,提取了故障特征信号。对经验模态分解过程进行了理论推导,证明经验模态分解可以分离出故障特征信号,给出了信号分离的充分条件。将这种方法应用于直升机减速器的两种故障(点蚀和裂纹)振动数据,结果表明经验模态分解正确地分离出了故障特征信号,信号特征更为显著。     

离心压气机不稳定流动的时频特征分析

离心压气机失稳过程是一个非常复杂的动态过程,提高离心压气机运行的可靠性需要准确获取失稳过程的时频特征。针对带无叶扩压器的高速离心压气机失稳过程中叶轮出口的动态压力数据,采用时空本征模态分解(STIMD)算法和经验模态分解(EMD)算法进行分解,得到了多个本征模态函数(IMF),并结合Hilbert变换对不稳定流动的动态特征进行分析。结果表明,STIMD算法得到了深喘和浅喘的时频信息,观察到了频率在150 Hz附近持续波动的失速现象,并捕捉到了浅喘先兆的频率曲线由抖动向恒定的过渡过程。STIMD算法改善了EMD的模态混叠问题,为压气机失稳分析提供了一种工具。