Framework and development of data-driven physics based model with application in dimensional accuracy prediction in pocket milling

In the manufacturing of thin wall components for aerospace industry, apart from the side wall contour error, the Remaining Bottom Thickness Error (RBTE) for the thin-wall pocket component (e.g. rocket shell) is of the same importance but overlooked in current research. If the RBTE reduces by 30%, the weight reduction of the entire component will reach up to tens of kilograms while improving the dynamic balance performance of the large component. Current RBTE control requires the off-process measurement of limited discrete points on the component bottom to provide the reference value for compensation. This leads to incompleteness in the remaining bottom thickness control and redundant measurement in manufacturing. In this paper, the framework of data-driven physics based model is proposed and developed for the real-time prediction of critical quality for large components, which enables accurate prediction and compensation of RBTE value for the thin wall components. The physics based model considers the primary root cause, in terms of tool deflection and clamping stiffness induced Axial Material Removal Thickness (AMRT) variation, for the RBTE formation. And to incorporate the dynamic and inherent coupling of the complicated manufacturing system, the multi-feature fusion and machine learning algorithm, i.e. kernel Principal Component Analysis (kPCA) and kernel Support Vector Regression (kSVR), are incorporated with the physics based model. Therefore, the proposed data-driven physics based model combines both process mechanism and the system disturbance to achieve better prediction accuracy. The final verification experiment is implemented to validate the effectiveness of the proposed method for dimensional accuracy prediction in pocket milling, and the prediction accuracy of AMRT achieves 0.014 mm and 0.019 mm for straight and corner milling, respectively.     

基于EMD熵特征融合的滚动轴承故障诊断方法

研究了滚动轴承故障诊断单一故障信号的局限性和故障特征的非线性,从信息融合的理论出发,利用非线性动力学参数熵作为特征,提出了基于经验模态分解(EMD)熵特征融合的方法来解决滚动轴承故障诊断问题.首先将原始信号进行EMD,利用EMD的自适应多分辨率的特点计算EMD得到的固有模态函数(IMF)信号的多种熵值,然后采用核主元分析(KPCA)对提取的状态特征进行信息融合,从而得到互补的特征,最后将提取的融合特征通过支持向量机(SVM)进行故障诊断.滚动轴承故障诊断实验表明:该方法结合了EMD、信息熵理论和KPCA强大的非线性处理能力的特点,可以进行滚动轴承故障诊断.      

轨姿控发动机动态推力与推力矢量测试系统研制

针对目前轨姿控发动机动态推力与推力矢量测试系统测量精度低、测试覆盖面不全及操作的复杂性,研制了一套基于压电测力平台的动态推力与推力矢量测试系统。此系统的研制攻克了测力平台结构参数优化技术、标准信号发生器设计技术、测试相间干扰处理技术等五项关键技术点.实现了连续脉冲力及多方向力的测量,系统操作可靠、便捷,各项测试指标满足总体要求,为动态推力及推力矢量测试提供了手段。     

含金属相变材料温控组件的研究

研究使用一种含有金属相变材料（Phase Change material,PCM）的温控组件（Thermal Control Component,TCC）对航天器上的有效载荷进行温度控制。通过对载荷的温控需求分析,设计并制备了以Ga-Sn合金为PCM的TCC,并通过地面模拟实验对TCC的温控性能进行测试。实验结果表明这种含有Ga-Sn合金的TCC满足载荷的温控需求。最后在仿真软件中建立了TCC的相变传热仿真模型用于预测TCC的温控性能。结果表明：仿真模型计算所得结果与实验结果基本一致,可用于预测TCC的温控性能。     

主成分分析在物联网产业发展中的应用研究

物联网由于其广泛的应用领域和广阔的发展前景,自诞生之日起就得到世界各国和科学界的研究和重视,本文通过主成分分析方法对物联网产业发展生产要素的分析,选取具有代表性的全国八省,从发展潜力层面进行物联网产业发展能力的综合评价研究,筛选出对物联网产业发展有重要作用的生产要素指标,对物联网产业的发展规划提供必要的理论依据和参考。     

航空发动机任务可靠性模型建立方法研究

任务可靠性模型是用以估计产品在执行任务过程中完成规定功能的概率,描述完成任务过程中产品各个单元的预定作用,用以度量工作有效性的一种模型。航空发动机作为复杂的机电产品,其使用功能和使用环境都较为复杂,行业内缺少航空发动机任务可靠性模型建立方法或指南。本文结合某型航空发动机的工程研制工作,尝试按照通用的任务可靠性模型建立方法,研究建立方法中各项分析工作的目的和意义及其相互之间的输入输出关系,摸索出适合工程应用的航空发动机任务可靠性模型的建立方法。     

The effect of spatial truncation error on variance of gravity anomalies derived from inversion of satellite orbital and gradiometric data

The spatial truncation error (STE) is a significant systematic error in the integral inversion of satellite gradiometric and orbital data to gravity anomalies at sea level. In order to reduce the effect of STE, a larger area than the desired one is considered in the inversion process, but the anomalies located in its central part are selected as the final results. The STE influences the variance of the results as well because the residual vector, which is contaminated with STE, is used for its estimation. The situation is even more complicated in variance component estimation because of its iterative nature. In this paper, we present a strategy to reduce the effect of STE on the a posteriori   variance factor and the variance components for inversion of satellite orbital and gradiometric data to gravity anomalies at sea level. The idea is to define two windowing matrices for reducing this error from the estimated residuals and anomalies. Our simulation studies over Fennoscandia show that the differences between the 0.5°×0.5°$0.5°\times 0.5°$ gravity anomalies obtained from orbital data and an existing gravity model have standard deviation (STD) and root mean squared error (RMSE) of 10.9 and 12.1 mGal, respectively, and those obtained from gradiometric data have 7.9 and 10.1 in the same units. In the case that they are combined using windowed variance components the STD and RMSE become 6.1 and 8.4 mGal. Also, the mean value of the estimated RMSE after using the windowed variances is in agreement with the RMSE of the differences between the estimated anomalies and those obtained from the gravity model.     

基于抗噪声ICA的高光谱数据特征提取方法

特征提取是高光谱数据应用的一个重要环节,用于将高光谱数据中具有特殊性质的地物分离出来并去除冗余信息.提出了一种使用独立成分分析(ICA,Independent Component Analysis)进行高光谱遥感地物特征提取的方法.为了解决ICA对噪声过分敏感的问题,采用最大噪声分量(MNF,Maximum Noise Fraction)算法替代传统的主成分分析对数据作降噪处理,由MNF引出的不完全独立成分分析(UICA,Undercomplete ICA)在不牺牲特征提取能力的情况下能够获得很高的运算效率.给出了HYDICE和PHI的数据试验结果,分别测试了算法在时间效率和特征提取能力方面的性能,证明了该算法具有预期的性能.     

中间相沥青基碳纤维表面特性和界面粘结性能

针对高模量、高热导率中间相沥青基碳纤维复合材料界面性能弱等瓶颈问题,深入研究该类纤维表面特性及其与树脂的界面粘结性能。选取3种典型中间相沥青基碳纤维,测试分析其微观形貌、表面能和极性与色散分量、表面元素种类与含量,利用微脱黏方法表征中间相沥青基碳纤维与环氧树脂的界面剪切强度。研究结果表明:中间相沥青基碳纤维表面均存在明显沟槽结构,但其呈化学惰性,选用的中间相沥青基碳纤维与环氧树脂界面剪切强度最高约为50 MPa,明显低于聚丙烯腈基碳纤维;纤维表面能越高,尤其是极性分量越高,中间相沥青基碳纤维/环氧树脂界面剪切强度越大,这些结果揭示了中间相沥青基碳纤维与树脂基体界面性能主控因素。     

遂宁机场起落航线发动机失效处置探讨

分析遂宁机场起落航线飞行区域地形特点以及单发飞机飞行性能数据,探讨起落航线迫降路线,为管制指挥与机组飞行准备提供参考,对确保飞行安全有重要的意义。