考虑人及组织因素的航天工程项目风险分析模型

针对人及组织因素是造成航天工程项目风险的重要原因,在揭示了人及组织因素产生航天工程项目风险的机理基础上,构建了考虑人及组织因素的航天工程项目风险分析模型。模型首先评价人及组织因素对航天工程元器件建造工作质量的影响,接着测度工作质量与不同缺陷类型之间的一致性概率,并应用Poisson分布计算缺陷严重性,最后根据贝叶斯定理对元器件先验失效率进行修正,通过一个经过数据处理的航天工程项目对所构建的模型和方法进行了验证。     

贝叶斯回归支持向量机的软测量建模方法

提出了一种基于贝叶斯证据框架的回归型支持向量机的参数选择和软测量建模方法。证据框架的第1层推理用来解释支持向量机的训练。证据框架的第2和第3层推理用于自动调整正则化参数和核参数并使其接近最优值。然后,将这种基于贝叶斯证据框架的回归型支持向量机用于估计聚丙烯腈生产过程的质量指标。仿真结果表明了该方法的有效性。     

连续分布的Bayes双样预测的通用公式

针对一般的连续分布,给出了Bayes双样预测所需的大小为N的未来样本的第j个（j=1,2,,N）顺序统计量的条件概率密度函数、条件可靠度、条件均值的通用公式,并将之应用于最常见的连续分布。     

基于贝叶斯理论的低循环疲劳寿命模型不确定性量化

为量化低循环疲劳寿命模型中的不确定性因素,利用贝叶斯理论,采用经典的模型校准形式确立了寿命模型的不确定性量化形式,并结合正态性检验对误差项进行验证;应用马尔可夫链-蒙特卡罗(MCMC)算法获得了模型参数后验分布的抽样样本,在小子样试验数据条件下确定了低循环疲劳寿命的95%不确定性区间,较好地覆盖了寿命的分散性;对参数样本进行了相关性分析,并将异方差回归概率模型与贝叶斯概率模型进行了比较。最后,利用Morris全局灵敏度分析方法获得了Manson-Coffin模型参数的全局灵敏度指标;同时,验证了在模型参数对先验信息敏感,或者说在先验信息影响极大的情况下,采用无信息先验处理方法的合理性。     

Aircraft air conditioning system health state estimation and prediction for predictive maintenance

The vast potential of system health monitoring and condition based maintenance on modern commercial aircraft is being realized through the innovative use of Airplane Condition Monitoring System(ACMS) data.However there are few methods addressing the issues of failure prognostics and predictive maintenance for commercial aircraft Air Conditioning System(ACS).This study developed a Bayesian failure prognostics approach using ACMS data for predictive maintenance of ACS.First, a health index characterizing the ACS health state is inferred from a multiple sensor signals using a data driven method.Then a dynamic linear model is proposed to describe the degradation process for failure prognostics.Bayesian inference formulas are carried out for degradation estimation and prediction.The developed approach is applied on a passenger aircraft fleet with ACMS data recorded for one year.The analysis of the case study shows that the developed method can produce satisfactory prognostics results, where all the ACS failure precursors are identified in advance, and the relative errors for the failure time prediction made when just entering the degradation warning stage are less than 8%.This would allow operators to proactively plan future maintenance.     

Assessment of the impact of dimensionality reduction methods on information classes and classifiers for hyperspectral image classification by multiple classifier system

Identification of the appropriate combination of classifier and dimensionality reduction method has been a recurring task for various hyperspectral image classification scenarios. Image classification by multiple classifier system has been evolving as a promising method for enhancing accuracy and reliability of image classification. Because of the diversity in generalization capabilities of various dimensionality reduction methods, the classifier optimal to the problem and hence the accuracy of image classification varies considerably. The impact of including multiple dimensionality reduction methods in the MCS architecture for the supervised classification of a hyperspectral image for land cover classification has been assessed in this study. Multi-source airborne hyperspectral images acquired over five different sites covering a range of land cover categories have been classified by a multiple classifier system and compared against the classification results obtained from support vector machines (SVM). The MCS offers acceptable classification results across the images or sites when there are multiple dimensionality reduction methods in addition to different classifiers. Apart from offering acceptable classification results, the MCS indicates about 5% increase in the overall accuracy when compared to the SVM classifier across the hyperspectral images and sites. Results indicate the presence of dimensionality reduction method specific empirical preferences by land cover categories for certain classifiers thereby demanding the design of MCS to support adaptive selection of classifiers and dimensionality reduction methods for hyperspectral image classification.     

动态分布参数验前分布的稳健性分析

讨论了动态分布参数的验前建模问题,并由此运用边缘分布给出验前分布的ML-Ⅱ估计;对于不同的验前分布所引起的验后稳健性问题作了初步分析。     

卫星多源信息贝叶斯融合方法研究

卫星在研制设计过程中存在大量不确定性因素,对卫星的可靠性及性能造成直接影响.如何实现复杂卫星系统的不确定性建模是目前亟需解决的问题,而多源分布信息融合是不确定性建模的关键.此外,在实际系统中,由于时间、成本的约束很难直接获得系统的分布信息.针对在系统信息缺失情况下如何实现卫星系统的多源信息融合问题,提出多层系统结构模块...