Identification and standardization of maneuvers based upon operational flight data

To find a way of loads analysis from operational flight data for advanced aircraft,maneuver identification and standardization jobs are conducted in this paper. For thousands of sorties from one aircraft, after studying the flight attitude when performing actions, the start and end time of the maneuvers can be determined. According to those time points, various types of maneuvers during the flight are extracted in the form of multi-parameters time histories. By analyzing the numerical range and curve shape of those parameters, a characteristic data library is established to model all types of maneuvers. Based on this library, a computer procedure using pattern-recognition theory is programmed to conduct automatic maneuver identification with high accuracy. In that way, operational loads are classified according to maneuver type. For a group of identified maneuvers of the same type, after the processes of time normalization, trace shifting, as well as averaging and smoothing, the idealization standard time history of each maneuver type is established.Finally, the typical load statuses are determined successfully based on standard maneuvers. The proposed method of maneuver identification and standardization is able to derive operational loads effectively, and might be applied to monitoring loads in Individual Aircraft Tracking Program(IATP).     

Multiple model particle flter track-before-detect for range ambiguous radar

The middle pulse repetition frequency（MPRF）and high pulse repetition frequency（HPRF）modes are widely adopted in airborne pulse Doppler（PD）radar systems,which results in the problem that the range measurement of targets is ambiguous.The existing data processing based range ambiguity resolving methods work well on the condition that the signal-to-noise ratio（SNR）is high enough.In this paper,a multiple model particle flter（MMPF）based track-beforedetect（TBD）method is proposed to address the problem of target detection and tracking with range ambiguous radar in low-SNR environment.By introducing a discrete variable that denotes whether a target is present or not and the discrete pulse interval number（PIN）as components of the target state vector,and modeling the incremental variable of the PIN as a three-state Markov chain,the proposed algorithm converts the problem of range ambiguity resolving into a hybrid state fltering problem.At last,the hybrid fltering problem is implemented by a MMPF-based TBD method in the Bayesian framework.Simulation results demonstrate that the proposed Bayesian approach can estimate target state as well as the PIN simultaneously,and succeeds in detecting and tracking weak targets with the range ambiguous radar.Simulation results also show that the performance of the proposed method is superior to that of the multiple hypothesis（MH）method in low-SNR environment.     

Study of amplification coefficient in a water-cooling Gardon-type heat power measuring apparatus

A new water-cooling Gardon-type heat power measuring apparatus is designed to meet the need of heat power source management and distribution. The steady state measurement mathematic model of the apparatus is built up in theory and the system amplification coefficient is defined as the ratio of the heat power to the temperature difference of the device, with which the value of the measured source power can be calculated easily with the corresponding temperature difference. In order to obtain an optimal heat power measuring system, the coefficients that can influence the relationship between the amplification coefficient, the temperature difference, and the heat power are analyzed. On the basis of these analyses, a set of experimental device is constructed and a number of experiments are carried on. Compared with the input heat power sample data, the error of the experimental measuring results is less than ±2%, and the experimental measuring values are in good agreement with the calculated theoretical ones. The heat power measuring apparatus can be applied in heat flux or heat power measurement in other fields due to its simple structure and high accuracy.     

Heteroscedastic regression analysis method for mixed data

The heteroscedastic regression model was established and the heteroscedastic regression analysis method was presented for mixed data composed of complete data,type-Ⅰ censored data and type-Ⅱ censored data from the location-scale distribution.The best unbiased estimations of regression coefficients,as well as the confidence limits of the location parameter and scale parameter were given.Furthermore,the point estimations and confidence limits of percentiles were obtained.Thus,the traditional multiple regression analysis method which is only suitable to the complete data from normal distribution can be extended to the cases of heteroscedastic mixed data and the location-scale distribution.So the presented method has a broad range of promising applications.     

Continual learning fault diagnosis:A dual-branch adaptive aggregation residual network for fault diagnosis with machine increments

As a data-driven approach, Deep Learning(DL)-based fault diagnosis methods need to collect the relatively comprehensive data on machine fault types to achieve satisfactory performance. A mechanical system may include multiple submachines in the real-world. During condition monitoring of a mechanical system, fault data are distributed in a continuous flow of constantly generated information and new faults will inevitably occur in unconsidered submachines, which are also called machine increments....     

Coupled Lagrangian impingement spray model for doublet impinging injectors under liquid rocket engine operating conditions

To predict the effect of the liquid rocket engine combustion chamber conditions on the impingement spray, the conventional uncoupled spray model for impinging injectors is extended by considering the coupling of the jet impingement process and the ambient gas field. The new coupled model consists of the plain-orifice sub-model, the jet-jet impingement sub-model and the droplet collision sub-model. The parameters of the child droplet are determined with the jet-jet impingement sub-model using correlations about the liquid jet parameters and the chamber conditions.The overall model is benchmarked under various impingement angles, jet momentum and offcenter ratios. Agreement with the published experimental data validates the ability of the model to predict the key spray characteristics, such as the mass flux and mixture ratio distributions in quiescent air. Besides, impinging sprays under changing ambient pressure and non-uniform gas flow are investigated to explore the effect of liquid rocket engine chamber conditions. First, a transient impingement spray during engine start-up phase is simulated with prescribed pressure profile. The minimum average droplet diameter is achieved when the orifices work in cavitation state, and is about 30% smaller than the steady single phase state. Second, the effect of non-uniform gas flow produces off-center impingement and the rotated spray fan by 38°. The proposed model suggests more reasonable impingement spray characteristics than the uncoupled one and can be used as the first step in the complex simulation of coupling impingement spray and combustion in liquid rocket engines.     

ERROR ESTIMATION AND DOUBLE MESH FOR RIGID-VISCOUS-PLASTIC FINITE ELEMENT ANALYSIS OF UNSTEADY EXTRUSION PROCESSES

Error estimation, double mesh as well as fore-and-aft process program are applied in the rigid-viscous-plastic finite element simulation of tube unsteady extrusions. By the error estimation, mesh can be reasonably divided. The double mesh includes analytical mesh and material mesh. The analytical mesh is used in the finite element analysis. The material mesh is used in the recording of distortion history. The fore-and-aft process program is used in the input-output of data and computer drawing. In the results, analytical meshes, distorted material meshes and strain contours are mapped by computer.     

Epistemic uncertainty quantification in flutter analysis using evidence theory

Aimed at evaluating the structural stability and flutter risk of the system, this paper manages to quantify epistemic uncertainty in flutter analysis using evidence theory, including both parametric uncertainty and method selection uncertainty, on the basis of information from limited experimental data of uncertain parameters. Two uncertain variables of the actuator coupling system with unknown probability distributions, that is bending and torsional stiffness, which are both described with multiple intervals and the basic belief assignment(BBA) extricated from the modal test of actuator coupling systems, are taken into account. Considering the difference in dealing with experimental data by different persons and the reliability of various information sources, a new combination rule of evidence––the generalized lower triangular matrices method is formed to acquire the combined BBA. Finally the parametric uncertainty and the epistemic uncertainty of flutter analysis method selection are considered in the same system to realize quantification. A typical rudder of missile is selected to examine the present method, and the dangerous range of velocity as well as relevant belief and plausibility functions is obtained. The results suggest that the present method is effective in obtaining the lower and upper bounds of flutter probability and assessing flutter risk of structures with limited experimental data of uncertain parameters and the belief of different methods.     

Unsteady aerodynamic modeling at high angles of attack using support vector machines

Accurate aerodynamic models are the basis of flight simulation and control law design.Mathematically modeling unsteady aerodynamics at high angles of attack bears great difficulties in model structure determination and parameter estimation due to little understanding of the flow mechanism.Support vector machines(SVMs)based on statistical learning theory provide a novel tool for nonlinear system modeling.The work presented here examines the feasibility of applying SVMs to high angle-of-attack unsteady aerodynamic modeling field.Mainly,after a review of SVMs,several issues associated with unsteady aerodynamic modeling by use of SVMs are discussed in detail,such as selection of input variables,selection of output variables and determination of SVM parameters.The least squares SVM(LS-SVM)models are set up from certain dynamic wind tunnel test data of a delta wing and an aircraft configuration,and then used to predict the aerodynamic responses in other tests.The predictions are in good agreement with the test data,which indicates the satisfying learning and generalization performance of LS-SVMs.     

直升机关键构件疲劳寿命监控中的核心技术

本文介绍了直升机结构疲劳寿命监控的技术和方法,指出了疲劳寿命监控的核心在于载荷谱的识别。并介绍了目前载荷谱监控的两种途径:直接载荷监控方法和飞行状态识别方法。最后根据我国实际情况,指出开展飞行状态识别研究,不适为一种适合我国情况行之有效的方法。     

航空发动机主轴承使用状态寿命预测模型

在滚动轴承寿命预测模型分析的基础上,提出了航空发动机主轴承状态寿命的概念,即把主轴承的使用寿命周期划分为状态良好、初步损伤、故障发展和即将失效4个寿命阶段;建立了1种利用机载传感器信息(转速、振动和飞行器机动),来确定航空发动机主轴承使用状态寿命的模型,该模型把状态监控数据和基于理论模型分析计算的方法相结合,为评估主轴承的寿命状态提供了1种新的方法.     

基于半随机滤波-期望最大化算法的剩余寿命在线预测

剩余寿命(RL)预测是设备预测维护的关键环节。准确在线预测能够为维护策略的实时安排提供更加精确的技术支持,有效避免失效的发生。工程实际中,反映设备退化过程的性能指标往往不能直接监测,为解决隐含退化过程的剩余寿命在线预测问题,提出一种基于半随机滤波-期望最大化(EM)算法的预测方法。首先以剩余寿命为隐含状态,构建状态空间模型描述直接监测数据与设备剩余寿命间的随机关系。为实现单个设备剩余寿命的在线预测,依据到当前时刻为止的监测数据,采用扩展卡尔曼滤波(EKF)与期望最大化算法相互协作的方法实时估计与更新模型未知参数和剩余寿命分布。最后,将该方法用于惯性测量组合(IMU)剩余寿命在线预测问题,实验结果表明该方法能够提高预测的准确性并减少预测的不确定性。     

面向航天器在轨装配的数字孪生技术

构建航天器在轨维修维护能力是确保空间系统长期稳定工作的有效途径,而对于空间环境中的在轨装配过程的模拟、监控、诊断和预测,目前的研究尚处于探索阶段,研究成果相对较少且缺乏整体解决方案。提出采用构建航天器数字孪生体的方式,来抽象表达航天器完成在轨装配的过程、状态和行为。首先分析了在轨装配航天器的结构组成及功能需求,然后系统阐述了航天器数字孪生体的数据组成、实现方式和作用,最后给出了航天器数字孪生体在设计、制造和在轨服务阶段的实施途径,并对航天器数字孪生体的作用进行了总结和展望。     

基于单片机和CdS涂层老化探头的装备腐蚀监测系统设计

针对装备对腐蚀状态监测需求,以 MSP320单片机为核心控制器、CdS涂层老化探头为腐蚀状态传感器,设计了一款 4路腐蚀状态监测系统,并用于某型车辆腐蚀状态监测。实验结果表明,该系统能真实监控该型车辆的腐蚀状态,为该车辆的预防性维修提供了重要技术和数据支持。同时,该系统具有安装灵活方便、工作可靠、体积小等优点,具有较高的推广应用价值。     

基于长短期记忆网络时序数据趋势预测及应用

为了研究状态监测大数据对设备运行状态的估计和预测,提出了一种人工经验与主成分分析相结合的长短期记忆网络方法（AEPCA-LSTM）,利用运行过程中的监测时序数据对设备运行趋势进行预测。首先,通过基于人工经验的主要成分分析方法（AEPCA）从状态监测系统中提取与目标变量最相关的状态变量作为输入;其次,利用长短期记忆网络（LSTM）对目标变量趋势变化进行预测,并考虑运行过程中新数据样本的持续产生,对模型进行定期更新,以提高模型的动态适应性。最后,将所提出的方法应用于船舶副机系统的涡轮增压器转速预测中,结果表明该方法相对于传统的PCA-LSTM和LSTM,具有更小的预测平均误差0.18037,即展现了其在时序数据趋势预测的优势。     

基于RF-SVR的燃油计量装置性能衰退检测和剩余寿命估计方法

为了实现航空发动机燃油系统的安全状态监测和健康管理,开展了燃油系统性能衰退检测和剩余使用寿命估计方面的研究。以燃油系统燃油计量装置为例,分析了其主要的性能衰退模式,设计了基于电流-速度数据的健康指标(HIs)选取方案,并考虑环境及模型参数不确定性,进行模型不确定性仿真,基于健康数据与性能衰退数据间的马氏距离对部件性能衰退进行检测。提出了基于随机森林-支持向量回归(RF-SVR)的剩余使用寿命(RUL)估计方法,利用通过RF特征选择优化的SVR模型实现部件RUL估计。最后基于某型民用涡扇发动机机械液压模型仿真数据对该方法进行了验证,结果表明:该方法的性能衰退检测虚警率及漏报率低于2%,RUL估计误差低于3%,可为航空发动机燃油系统的预测性维护提供参考。      

多传感器监测飞机部件非线性退化评估

飞机部件一般采用多传感器进行状态监控,针对退化过程具有非线性特征的民机典型部件剩余寿命（RUL）预测及评估问题,首先建立了部件性能参数的一般非线性Wiener退化过程,推导出基于多传感器监测数据的剩余寿命预测框架和概率密度函数,随后利用状态空间模型进行隐退化状态估计并同时利用最大期望算法（EM）实现参数递推估计,最后形成了飞机部件多传感器监测下的剩余寿命非线性退化评估方法。通过数值仿真案例和民航发动机剩余寿命预测案例,对比线性退化模型和基于单一传感器监测数据的非线性退化模型,验证了所提方法在提高剩余寿命预测精度的有效性,可为飞机及其部件的剩余使用寿命预测和视情维护决策提供技术支撑。     

精密机电设备寿命评估方法

寿命评估技术既是确保设备安全、可靠运行的核心和关键,也是设备定寿、延寿的重要依据。从工程的视角,根据精密机电设备的工作状态,将精密机电设备分为新研设备、工作态设备、贮存态设备3类,分析这3种状态机电设备的特点及可以获取的信息资源,进而对新研设备、工作态设备、贮存态设备的寿命评估方法进行了系统研究和评述,对存在的问题及未来发展趋势进行了分析和展望。鉴于难以对复杂精密机电设备的机理进行建模,数据驱动的寿命估计已成为当前研究的主流,本文在3种状态机电设备分类的框架下,重点分析了数据驱动寿命估计的研究动态。     

大涵道比涡扇发动机涡轮监视温度容错解析

提出了对大涵道比涡扇发动机涡轮监视温度的容错解析方法,以避免由于涡轮监视温度传感器故障造成的发动机控制降级,或未检测到故障导致发动机超温.解析方法包含两种:一种是基于涡轮排气总温的解析方法作为主解析方法;另一种是基于空气流量模型的解析方法作为余度解析方法,用于在主解析温度与测量温度差异较大时进行比较取真.使用发动机试车数据进行了方法验证,结果显示:主解析方法的稳态计算误差不超过0.33%;余度解析方法的稳态计算误差不超过0.8%.这表明两种解析方法都是有效的,可以用于发动机容错控制.