In practice, the failure rate of most equipment exhibits different tendencies at different stages and even its failure rate curve behaves a multimodal trace during its life cycle. As a result,traditionally evaluating ...In practice, the failure rate of most equipment exhibits different tendencies at different stages and even its failure rate curve behaves a multimodal trace during its life cycle. As a result,traditionally evaluating the reliability of equipment with a single model may lead to severer errors.However, if lifetime is divided into several different intervals according to the characteristics of its failure rate, piecewise fitting can more accurately approximate the failure rate of equipment. Therefore, in this paper, failure rate is regarded as a piecewise function, and two kinds of segmented distribution are put forward to evaluate reliability. In order to estimate parameters in the segmented reliability function, Bayesian estimation and maximum likelihood estimation（MLE） of the segmented distribution are discussed in this paper. Since traditional information criterion is not suitable for the segmented distribution, an improved information criterion is proposed to test and evaluate the segmented reliability model in this paper. After a great deal of testing and verification,the segmented reliability model and its estimation methods presented in this paper are proven more efficient and accurate than the traditional non-segmented single model, especially when the change of the failure rate is time-phased or multimodal. The significant performance of the segmented reliability model in evaluating reliability of proximity sensors of leading-edge flap in civil aircraft indicates that the segmented distribution and its estimation method in this paper could be useful and accurate.展开更多
本文的目的是在大数据框架下,系统地陈述了如何利用吉布斯抽样(Gibbs sam pling)方法作为工具,以样本误差容忍度为标准的大数据关联特征因子提取的推断原理为基础,在金融衍生品场景下对关联风险特征进行有效提取的思维和路径.具体来讲,...本文的目的是在大数据框架下,系统地陈述了如何利用吉布斯抽样(Gibbs sam pling)方法作为工具,以样本误差容忍度为标准的大数据关联特征因子提取的推断原理为基础,在金融衍生品场景下对关联风险特征进行有效提取的思维和路径.具体来讲,采用马尔可夫链蒙特卡罗(MCMC)框架下的吉布斯抽样(Gibbs sampling)算法为工具,通过"OR值"(odds ratio)(也称为"比值比"或"优势比")为验证标准,在大数据(包含传统的结构化和非结构化数据)一般框架下对高度关联特征因子提取推断的原理下,系统性地陈述了如何从海量数据中提取与金融衍生品价格或者风险关联度高的风险特征因子的随机搜索方法.为了能够比较全面地展示如何利用吉布斯抽样方法通过随机搜索算法来实现对金融衍生品风险特征的提取,在本文中,我们对三种金融产品的关联特征提取进行了比较全面和系统的讨论,他们是:1)对支持"基金中的基金"(fund of funds,简称"FOF")组建的影响基金业绩关联特征的挖掘;2)对影响大宗商品期货螺纹钢价格趋势变化的关联特征指标的挖掘;3)对影响大宗商品期货铜价格趋势分析的关联特征刻画的提取.本文的分析和实证结果表明,我们在大数据框架下建立的特征提取方法除了能够有效地筛选出刻画影响基金业绩的关联特征外,也够提取出影响螺纹钢期货和铜期货价格趋势变化的关联特征,这为业界对FOF的组建与管理,对应金融衍生品价格变化走势,特别是大宗期货交易和风险管理方面提供了一种新的分析维度和风险特征因子应用方向.另外,本文讨论的从大数据的视角筛选金融衍生品风险特征因子的方法,也与过去传统的计量分析方法不同,是金融科技在大数据金融方面分析和应用的创新点.展开更多
基金supported by the National Natural Science Foundation of China (Nos. 60672164, 60939003, 61079013, 60879001, 90000871)the Special Project about Humanities and Social Sciences in Ministry of Education of China (No. 16JDGC008)+2 种基金National Natural Science Funds and Civil Aviation Mutual Funds (Nos. U1533128 and U1233114)Study On Reusing Sketch User Interface Oriented Design Knowledge (No. 16KJA520003)Six Talent Peaks Project In Jiangsu Province (No. 2016-XYDXXJS-088)
文摘In practice, the failure rate of most equipment exhibits different tendencies at different stages and even its failure rate curve behaves a multimodal trace during its life cycle. As a result,traditionally evaluating the reliability of equipment with a single model may lead to severer errors.However, if lifetime is divided into several different intervals according to the characteristics of its failure rate, piecewise fitting can more accurately approximate the failure rate of equipment. Therefore, in this paper, failure rate is regarded as a piecewise function, and two kinds of segmented distribution are put forward to evaluate reliability. In order to estimate parameters in the segmented reliability function, Bayesian estimation and maximum likelihood estimation（MLE） of the segmented distribution are discussed in this paper. Since traditional information criterion is not suitable for the segmented distribution, an improved information criterion is proposed to test and evaluate the segmented reliability model in this paper. After a great deal of testing and verification,the segmented reliability model and its estimation methods presented in this paper are proven more efficient and accurate than the traditional non-segmented single model, especially when the change of the failure rate is time-phased or multimodal. The significant performance of the segmented reliability model in evaluating reliability of proximity sensors of leading-edge flap in civil aircraft indicates that the segmented distribution and its estimation method in this paper could be useful and accurate.
文摘本文的目的是在大数据框架下,系统地陈述了如何利用吉布斯抽样(Gibbs sam pling)方法作为工具,以样本误差容忍度为标准的大数据关联特征因子提取的推断原理为基础,在金融衍生品场景下对关联风险特征进行有效提取的思维和路径.具体来讲,采用马尔可夫链蒙特卡罗(MCMC)框架下的吉布斯抽样(Gibbs sampling)算法为工具,通过"OR值"(odds ratio)(也称为"比值比"或"优势比")为验证标准,在大数据(包含传统的结构化和非结构化数据)一般框架下对高度关联特征因子提取推断的原理下,系统性地陈述了如何从海量数据中提取与金融衍生品价格或者风险关联度高的风险特征因子的随机搜索方法.为了能够比较全面地展示如何利用吉布斯抽样方法通过随机搜索算法来实现对金融衍生品风险特征的提取,在本文中,我们对三种金融产品的关联特征提取进行了比较全面和系统的讨论,他们是:1)对支持"基金中的基金"(fund of funds,简称"FOF")组建的影响基金业绩关联特征的挖掘;2)对影响大宗商品期货螺纹钢价格趋势变化的关联特征指标的挖掘;3)对影响大宗商品期货铜价格趋势分析的关联特征刻画的提取.本文的分析和实证结果表明,我们在大数据框架下建立的特征提取方法除了能够有效地筛选出刻画影响基金业绩的关联特征外,也够提取出影响螺纹钢期货和铜期货价格趋势变化的关联特征,这为业界对FOF的组建与管理,对应金融衍生品价格变化走势,特别是大宗期货交易和风险管理方面提供了一种新的分析维度和风险特征因子应用方向.另外,本文讨论的从大数据的视角筛选金融衍生品风险特征因子的方法,也与过去传统的计量分析方法不同,是金融科技在大数据金融方面分析和应用的创新点.
