模糊故障树分析法在航空发动机滑油渗漏分析中的应用

目的：为准确查找航空发动机滑油渗漏原因,研究模糊故障树理论在飞机故障诊断方面的应用。方法：以CFM56-7B发动机滑油系统为研究对象,将故障树理论应用于滑油系统渗漏故障诊断中,采用下行法求解最小割集得到故障诱因最小事件。根据滑油故障发生概率具有模糊和不确定性特点,结合Delphi专家调查采用模糊数学理论进行定量分析,确定故障诱因事件风险重要度排序。结果：从CFM56-7B滑油渗漏故障原因重要度排序可知,故障诱因依次为后收油池供油口盖封严损坏（x1）、后收油池滑油回油管接头处裂纹(x2)、发动机使用MJO291滑油后导致密封材料损坏（x8)等,维修时应注意。结论：维修单位故障记录验证了方法的有效性,可为准确和快速诊断和排除滑油渗漏故障提供参考。     

非等间隔阶跃灰色模型在滑油光谱分析中的应用

针对非等间隔、具有阶跃趋势的原始数据序列建立了非等间隔阶跃灰色模型 ,模型参数的辨识采用了非线性最小二乘估计的Levenberg Marquardt算法。将所建模型用于 16V2 80ZJA型机车柴油机滑油的光谱分析数据的建模 ,得到了较高的拟合精度 ,尤其在阶跃点附近 ,大大改善了拟合精度。利用模型参数对换油后的测试数据进行修正 ,有利于提高光谱分析的准确度和可靠性。     

基于新陈代谢双时序模型的激光多普勒测速仪漂移数据滤波

为了有效抑制激光多普勒测速仪输出数据的随机漂移,提高其测量精度,在传统时序模型的基础上采用新陈代谢双时序模型进行激光多普勒测速仪漂移数据滤波.该模型由两级新陈代谢时序模型级联而成,每一级新陈代谢时序模型均依次对13个数据点时序建模.依据此模型分别对激光多普勒测速仪静态及动态漂移数据进行建模和滤波.利用方差分析法及Allan方差法对滤波前后的测速仪静态漂移数据进行分析并利用频谱分析法对比了滤波前后的测速仪动态漂移数据.结果表明:新陈代谢双时序模型将静态漂移数据标准差减小为原始数据的44%,将角度随机游走降为原始数据的41%;该方法不仅能实时降低激光多普勒测速仪的静态随机漂移误差,而且能够实时有效抑制其动态输出噪声.     

连接时序分类准则声学建模方法优化

对基于连接时序分类准则(connectionist temporal classification,CTC)的端到端声学建模方法进行研究和优化。研究分析了不同声学特征、建模单元以及神经网络结构对CTC声学模型性能的影响,针对CTC模型中blank符号共享导致的建模缺陷提出了建模单元相关的非共享blank方法进行改进,并引入融合建模单元关联信息的模型初始化方法进一步提高CTC模型的性能。在300小时标准英文数据集Switchboard的实验结果显示,结合非共享blank、时延神经网络以及融合建模单元关联信息的初始化方法,CTC声学模型相对于基线系统在词错误率上取得绝对1.1%的下降,同时在训练速度上取得3.3倍的提高,实验结果证明本文针对端到端声学建模提出的优化方法是有效的。      

新一代运载火箭时序仿真系统信号完整性分析

新一代运载火箭时序仿真系统具有数字电路速度快、集成度高的特点,系统要求发出多路高精度时序、时串信号以满足新一代运载火箭地面测试设备的检查与校准需求,因此信号完整性问题在系统设计中不容忽视。针对仿真系统的典型模块(USB 3.0 Super-speed差分线、FPGA外设数据走线、时钟走线)进行建模分析仿真得出PCB硬件电路设计参数,给出时序仿真系统设计信号完整性问题的抑制和解决方法,优化了板级信号质量,改善系统可靠性、工作连续性和输出精度,可有效提高新一代运载火箭测试效率和测试可靠性。     

可见/近红外光谱的油茶籽油三元体系掺假检测模型优化

采用可见/近红外光谱技术结合化学计量学方法对油茶籽油三元体系掺假进行定量检测研究。将菜籽油和花生油按不同比例掺入纯油茶籽油中,获得掺假样本。采集纯油茶籽油及掺假样本在350~1 800 nm范围内的可见/近红外光谱数据,随机分为校正集和预测集,并从不同建模波段、预处理方法及建模方法角度对掺假预测模型进行优化。研究结果表明,菜籽油、花生油和总掺伪量的最优建模波段及预处理方法分别为750~1 770,900~1 770 ,870~1 770 nm和多元散射校正(MSC)、标准归一化处理(SNV)和二阶微分,而最优的建模方法均为最小二乘支持向量机(LSSVM)。对于最优掺假模型,菜籽油、花生油和总掺伪量的预测集相关系数(R_p)和预测均方根误差(RMSEP)分别为0.963,0.982,0.993和2.1%,1.5%,1.8%。由此可见,可见/近红外光谱技术结合化学计量学方法可以用于油茶籽油的三元体系掺假定量检测。     

基于PXI总线多通道时序监测电路系统设计

本文通过对箭载发动机的时序监测方法进行研究、分析,设计出一款基于PXI总线多通道时序检测电路系统。从产品的设计原理,设计方法到试验及结果分析等方面对时序监测电路系统进行了详细的分析。时序监测电路系统已经应用在导弹和火箭发射地面测试发射控制系统中,经过多次测试和发射场真实发射试验验证,达到了预期效果。     

小工具数控抛光对元件表面中频误差的匀滑研究

数控抛光已被广泛应用于光学元器件的加工制造,而抑制元件表面中频误差是加工过程中一项十分重要的内容。基于Presston方程对数控小工具抛光盘去除函数进行了建模,得到了理论化的去除函数表达式。结合去除函数,在参数化匀滑模型基础上通过建立多参数的时变理论模型,表明元件表面中频误差是随抛光过程呈指数型收敛的,其收敛效率取决于材料参数、体积去除率等抛光工艺参数。对理论模型的匀滑曲线进行了模拟分析,实现了不同工艺条件下的匀滑效率的对比。结果表明:在不同抛光盘材料的匀滑过程中,材料系数越大,其整体匀滑效率越高。同样,抛光盘体积去除率越大,对表面误差的匀滑效率也会越高。进行了一组空间周期分别为3,5,7 mm的波纹误差的匀滑实验,其结果表明,在相同的抛光参数下,具有较大空间频率的波纹匀滑效率会更高,收敛曲线下降得更快。最后对比了不同材料抛光盘匀滑效率,从实验上证实了沥青盘在波纹匀滑效率上远高于聚氨酯材料的抛光盘。     

诱导空间非相干束匀滑技术的近区特性及改善技术

诱导空间非相干技术是面向激光驱动惯性约束核聚变的一种具有自身独特优势的束匀滑方法.然而直接使用诱导空间非相干方法将引起强烈的近区强度空间调制,这将威胁装置的运行安全,并严重限制装置的最大输出能力.这也是该方法应用于聚变级高功率激光装置的主要技术障碍之一.本文介绍了一种通过双透镜滤波系统对诱导空间非相干束匀滑技术导致的近区空间强度调制进行匀滑的技术.利用该技术可以在保留诱导空间非相干束匀滑方法的先天优势(更好的远区匀滑特性)的前提下,获得均匀、稳定的近区强度分布,从而避免高功率激光系统在使用诱导空间非相干束匀滑技术时,因为近区强度不均匀、不稳定导致的器件损伤及输出能力受限.在理论建模和数值分析的基础上,以近区调制度、软化因子和透过率为主要评价指标,对比分析了方形、圆形、高斯型等3种滤波孔在不同尺寸下的近区输出效果,最终给出了一种典型的优化结果:16×16诱导空间非相干分割数、0.8倍衍射极限宽度、方形小孔.此时近区强度分布均匀,同时保证了较好的远区匀滑效果和高的能量利用率.在此基础上,针对装置的实际应用情况,进一步分析了准直误差对近区强度分布的影响,结果表明准直误差小于0.1倍衍射极限便不会影响输出的近区质量.对诱导空间非相干束匀滑方法所得焦斑的模拟分析表明,滤波系统的加入能进一步改善焦斑的低频不均匀性.     

光纤陀螺随机漂移建模及其在惯导误差方程中的应用

针对光纤陀螺随机漂移模型时序无法直接应用卡尔曼滤波分析的问题,在对陀螺漂移建模分析的基础上,利用一阶马尔柯夫过程等效拟合陀螺漂移模型,并通过Allan方差分析拟合的合理性。对惯导系统误差方程进行了状态扩充,为进一步实现导航系统的滤波分析奠定了理论基础。     

Forecasting the underlying potential governing the time series of a dynamical system

We introduce a technique of time series analysis, potential forecasting, which is based on dynamical propagation of the probability density of time series. We employ polynomial coefficients of the orthogonal approximation of the empirical probability distribution and extrapolate them in order to forecast the future probability distribution of data. The method is tested on artificial data, used for hindcasting observed climate data, and then applied to forecast Arctic sea-ice time series. The proposed methodology completes a framework for ‘potential analysis’ of tipping points which altogether serves anticipating, detecting and forecasting nonlinear changes including bifurcations using several independent techniques of time series analysis. Although being applied to climatological series in the present paper, the method is very general and can be used to forecast dynamics in time series of any origin.     

A granular time series approach to long-term forecasting and trend forecasting

To overcome the “curse of dimensionality” (which plagues most predictors (predictive models) when carrying out long-term forecasts) and cope with uncertainty present in many time series, in this study, we introduce a concept of granular time series which are used to long-term forecasting and trend forecasting. A technique of fuzzy clustering is used to construct information granules on a basis of available numeric data present in the original time series. In the sequel, we develop a forecasting model which captures the essential relationships between such information granules and in this manner constructs a fundamental forecasting mechanism. It is demonstrated that the proposed model comes with a number of advantages which manifest when processing a large number of data. Experimental evidence is provided through a series of examples using which we quantify the performance of the forecasting model and provide with some comparative analysis.     

A refined fuzzy time series model for stock market forecasting

Time series models have been used to make predictions of stock prices, academic enrollments, weather, road accident casualties, etc. In this paper we present a simple time-variant fuzzy time series forecasting method. The proposed method uses heuristic approach to define frequency-density-based partitions of the universe of discourse. We have proposed a fuzzy metric to use the frequency-density-based partitioning. The proposed fuzzy metric also uses a trend predictor to calculate the forecast. The new method is applied for forecasting TAIEX and enrollments’ forecasting of the University of Alabama. It is shown that the proposed method work with higher accuracy as compared to other fuzzy time series methods developed for forecasting TAIEX and enrollments of the University of Alabama.     

基于周期性截断灰色系统的电力负荷预测

电力负荷预测是电力系统调度和电力生产计划制定的重要依据。电力负荷时间序列有着明显的周期性特征。传统的电力负荷预测主要侧重于预测方法的研究,而忽略了电力负荷数据周期性特性的分析,影响了预测的准确性。针对电力负荷时间序列的周期性特征,提出了一种基于周期性截断的灰色系统模型来进行电力负荷预测。该模型利用周期性截断来反映负荷数据的周期性特征,提高了预测的精度。仿真采用EUNITE Network的公开负荷数据进行算法性能的测试,并与一些主流的电力负荷预测算法：BP神经网络、极限学习机、自回归模型以及传统的灰色系统模型做比较。仿真结果表明,周期性截断的灰色系统负荷预测的归一化均方误差和绝对平均误差是最小的。周期性截断的灰色系统为电力系统负荷预测提供了一种新的有效方法。     

Dynamics of electricity market correlations

Electricity market participants rely on demand and price forecasts to decide their bidding strategies, allocate assets, negotiate bilateral contracts, hedge risks, and plan facility investments. However, forecasting is hampered by the non-linear and stochastic nature of price time series. Diverse modeling strategies, from neural networks to traditional transfer functions, have been explored. These approaches are based on the assumption that price series contain correlations that can be exploited for model-based prediction purposes. While many works have been devoted to the demand and price modeling, a limited number of reports on the nature and dynamics of electricity market correlations are available. This paper uses detrended fluctuation analysis to study correlations in the demand and price time series and takes the Australian market as a case study. The results show the existence of correlations in both demand and prices over three orders of magnitude in time ranging from hours to months. However, the Hurst exponent is not constant over time, and its time evolution was computed over a subsample moving window of 250 observations. The computations, also made for two Canadian markets, show that the correlations present important fluctuations over a seasonal one-year cycle. Interestingly, non-linearities (measured in terms of a multifractality index) and reduced price predictability are found for the June-July periods, while the converse behavior is displayed during the December-January period. In terms of forecasting models, our results suggest that non-linear recursive models should be considered for accurate day-ahead price estimation. On the other hand, linear models seem to suffice for demand forecasting purposes.     

基于模糊模型支持向量机的混沌时间序列预测

基于支持向量机强大的非线性映射能力和模糊逻辑易于将先验的系统知识结合到模糊规则的 特性, 根据混沌动力系统的相空间重构理论, 提出了一种混沌时间序列的模糊模型的支持向 量机预测模型,并采用适用于大规模问题求解的最小二乘法来训练预测模型,利用该模型分别 对模型的整体预测性能与嵌入维数及延迟时间的关系进行了探讨.最后利用Mackey-Glass时 间序列和典型的Lorenz系统生成的时间序列对该模型进行了验证,结果表明该预测模型不仅 能够自动的从学习数据中获取知识产生模糊规则，提取能够代表混沌时间序列内在规律的支 持向量，大大减少支持向量的数目，精确地预测未来的混沌时间序列,而且在混沌时间序列 的嵌入维数未知和延迟时间不能合理选择的情况下,也能取得比较好的预测效果.这一结论预 示着基于模糊模型的支持向量机是一种研究混沌时间序列的有效方法. 关键词： 模糊模型 混沌时间序列 支持向量机 最小二乘法     

运载火箭类周期测试数据特征预测方法研究

鉴于我国运载火箭测试数据判读工作现状,研究测试数据的预测算法,有助于预判故障趋势,提前采取措施。分析了运载火箭测试数据,提出测试数据依时间序列的分类方法;针对类周期型数据,设计了相应的特征提取算法,得出数据特征时间序列;应用滚动自回归预测算法,并将历史实际值与预测值的加权值作为当前时刻的建模数据,实现了类周期数据特征的趋势预测。该方法有助于改进运载火箭类周期型数据判读方法。     

Application of the nonlinear time series prediction method of genetic algorithm for forecasting surface wind of point station in the South China Sea with scatterometer observations

The present work reports the development of nonlinear time series prediction method of genetic algorithm(GA) with singular spectrum analysis(SSA) for forecasting the surface wind of a point station in the South China Sea(SCS) with scatterometer observations.Before the nonlinear technique GA is used for forecasting the time series of surface wind,the SSA is applied to reduce the noise.The surface wind speed and surface wind components from scatterometer observations at three locations in the SCS have been used to develop and test the technique.The predictions have been compared with persistence forecasts in terms of root mean square error.The predicted surface wind with GA and SSA made up to four days(longer for some point station) in advance have been found to be significantly superior to those made by persistence model.This method can serve as a cost-effective alternate prediction technique for forecasting surface wind of a point station in the SCS basin.     

Nonlinear Time Series Forecast Using Radial Basis Function Neural Networks

In the research of using Radial Basis Function Neural Network (RBF NN) forecasting nonlinear time series, we investigate how the different clusterings affect the process of learning and forecasting. We find that k-means clustering is very suitable. In order to increase the precision we introduce a nonlinear feedback term to escape from the local minima of energy, then we use the model to forecast the nonlinear time series which are produced by Mackey-Glass equation and stocks. By selecting the k-means clustering and the suitable feedback term, much better forecasting results are obtained.     

Control modeling of ash wood drying using process neural networks

For the control and system identification problems of the deceleration phase of the ash wood drying process, we propose a deceleration phase modeling method of ash wood drying using process neural networks with double hidden layers. This method applies time-varying characteristics of process neural networks and the ability to extract time-space cumulative effects. The time-varying characteristics of wood drying deceleration phase modeling under time series background are directly incorporated into the model. By comparison with traditional neural network modeling results, we prove that the model of process neural networks has better control accuracy, providing an idea to solve control and nonlinear system identification problems under a time series background.