Starter control valve failure prediction machine to predict and trend starter control valve failures in gas turbine engines using a starter control valve health prognostic, program product and related methods
    3.
    发明授权
    Starter control valve failure prediction machine to predict and trend starter control valve failures in gas turbine engines using a starter control valve health prognostic, program product and related methods 有权
    起动控制阀故障预测机预测和趋势使用启动器控制阀健康预测的燃气轮机发动机控制阀故障,程序产品及相关方法

    公开(公告)号:US08370045B2

    公开(公告)日:2013-02-05

    申请号:US12541811

    申请日:2009-08-14

    IPC分类号: G06F19/00

    摘要: Starter control valve failure prediction machines, systems, program products, and computer implemented methods to predict and trend starter control valve failures in gas turbine engines using a starter control valve health prognostic and to make predictions of starter control valve failures, are provided. A computer implemented method according to an embodiment of the present invention can include the steps of generating a continuous starter control valve deterioration trend function responsive to a plurality of health indices derived from gas turbine engine startup data downloaded from gas turbine engine sensors for a plurality of startups and analyzing the continuous starter control valve deterioration trend function to identify potential starter control valve failure points where the points on the starter control valve deterioration trend function correlate to a starter control valve health prognostic responsive to historic gas turbine engine startup data downloaded from gas turbine engine sensors.

    摘要翻译: 提供了起动器控制阀故障预测机,系统,程序产品和计算机实现的方法来预测和趋势,使用启动器控制阀健康预测的燃气涡轮发动机中的起动器控制阀故障并预测起动器控制阀故障。 根据本发明的实施例的计算机实现的方法可以包括以下步骤:响应于从燃气涡轮发动机传感器下载的燃气涡轮发动机启动数据导出的多个健康指数,产生连续起动器控制阀恶化趋势功能,用于多个 启动和分析连续起动器控制阀恶化趋势功能,以识别潜在的起动器控制阀故障点,其中起动器控制阀的点变化趋势功能与启动器控制阀的健康预测相关,响应于从燃气轮机下载的历史燃气涡轮发动机启动数据 发动机传感器。

    Starter Control Valve Failure Prediction Machine To Predict and Trend Starter Control Valve Failures In Gas Turbine Engines Using A Starter Control Valve Health Prognostic, Program Product and Related Methods
    5.
    发明申请
    Starter Control Valve Failure Prediction Machine To Predict and Trend Starter Control Valve Failures In Gas Turbine Engines Using A Starter Control Valve Health Prognostic, Program Product and Related Methods 有权
    起动器控制阀故障预测机预测和趋势起动器控制阀故障在使用起动器控制阀的燃气轮机发动机健康预测,程序产品和相关方法

    公开(公告)号:US20110040470A1

    公开(公告)日:2011-02-17

    申请号:US12541811

    申请日:2009-08-14

    IPC分类号: G06F19/00

    摘要: Starter control valve failure prediction machines, systems, program products, and computer implemented methods to predict and trend starter control valve failures in gas turbine engines using a starter control valve health prognostic and to make predictions of starter control valve failures, are provided. A computer implemented method according to an embodiment of the present invention can include the steps of generating a continuous starter control valve deterioration trend function responsive to a plurality of health indices derived from gas turbine engine startup data downloaded from gas turbine engine sensors for a plurality of startups and analyzing the continuous starter control valve deterioration trend function to identify potential starter control valve failure points where the points on the starter control valve deterioration trend function correlate to a starter control valve health prognostic responsive to historic gas turbine engine startup data downloaded from gas turbine engine sensors.

    摘要翻译: 提供了起动控制阀故障预测机,系统,程序产品和计算机实现的方法来预测和趋势,使用启动器控制阀健康预测的燃气轮机发动机起动器控制阀故障,并预测起动器控制阀故障。 根据本发明的实施例的计算机实现的方法可以包括以下步骤:响应于从燃气涡轮发动机传感器下载的燃气涡轮发动机启动数据导出的多个健康指数,产生连续起动器控制阀恶化趋势功能,用于多个 启动和分析连续起动器控制阀恶化趋势功能,以识别潜在的起动器控制阀故障点,其中起动器控制阀的点变化趋势功能与启动器控制阀的健康预测相关,响应于从燃气轮机下载的历史燃气涡轮发动机启动数据 发动机传感器。

    SYSTEM AND PROCESS FOR A FUSION CLASSIFICATION FOR INSURANCE UNDERWRITING SUITABLE FOR USE BY AN AUTOMATED SYSTEM
    6.
    发明申请
    SYSTEM AND PROCESS FOR A FUSION CLASSIFICATION FOR INSURANCE UNDERWRITING SUITABLE FOR USE BY AN AUTOMATED SYSTEM 有权
    用于保险分类的系统和程序,适用于自动系统使用的保险

    公开(公告)号:US20090048876A1

    公开(公告)日:2009-02-19

    申请号:US12131545

    申请日:2008-06-02

    IPC分类号: G06Q40/00 G06Q10/00

    CPC分类号: G06Q40/08 G06Q40/00

    摘要: A method and system for fusing a collection of classifiers used for an automated insurance underwriting system and/or its quality assurance is described. Specifically, the outputs of a collection of classifiers are fused. The fusion of the data will typically result in some amount of consensus and some amount of conflict among the classifiers. The consensus will be measured and used to estimate a degree of confidence in the fused decisions. Based on the decision and degree of confidence of the fusion and the decision and degree of confidence of the production decision engine, a comparison module may then be used to identify cases for audit, cases for augmenting the training/test sets for re-tuning production decision engine, cases for review, or may simply trigger a record of its occurrence for tracking purposes. The fusion can compensate for the potential correlation among the classifiers. The reliability of each classifier can be represented by a static or dynamic discounting factor, which will reflect the expected accuracy of the classifier. A static discounting factor is used to represent a prior expectation about the classifier's reliability, e.g., it might be based on the average past accuracy of the model, while a dynamic discounting is used to represent a conditional assessment of the classifier's reliability, e.g., whenever a classifier bases its output on an insufficient number of points it is not reliable.

    摘要翻译: 描述用于融合用于自动保险承保系统的分类器集合和/或其质量保证的方法和系统。 具体来说,分类器的集合的输出被融合。 数据的融合通常会导致一些共识和分类器之间的一些冲突。 共识将被测量并用于估计融合决策的信心程度。 根据融合的决定和信心程度以及生产​​决策引擎的决策和决策程度,然后可以使用比较模块来识别审计案例,增加用于重新调整生产的培训/测试集的案例 决策引擎,审查案例,或者可以简单地触发其发生记录以进行跟踪。 融合可以补偿分类器之间的潜在相关性。 每个分类器的可靠性可以由静态或动态折扣因子表示,这将反映分类器的预期准确性。 静态折扣因子用于表示对分类器的可靠性的先前期望,例如,可以基于模型的平均过去精度,而使用动态贴现来表示分类器的可靠性的条件评估,例如,每当 分类器的输出基于不可靠的点数不足。

    Systems and methods for efficient frontier supplementation in multi-objective portfolio analysis
    7.
    发明授权
    Systems and methods for efficient frontier supplementation in multi-objective portfolio analysis 失效
    多目标投资组合分析中有效前沿补充的系统和方法

    公开(公告)号:US07469228B2

    公开(公告)日:2008-12-23

    申请号:US10781897

    申请日:2004-02-20

    IPC分类号: G06Q40/00

    CPC分类号: G06Q40/06

    摘要: The systems and methods of the invention are directed to portfolio optimization and related techniques. For example, the invention provides a method for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem, the method comprising: performing a first multi-objective optimization process, based on competing objectives, to generate an efficient frontier of possible solutions; observing the generated efficient frontier; based on the observing, identifying an area of the efficient frontier in which there is a gap; and effecting a gap filling process by which the efficient frontier is supplemented in the area of the gap, the efficient frontier being used in investment decisioning.

    摘要翻译: 本发明的系统和方法针对组合优化和相关技术。 例如,本发明提供了一种用于基于竞争目标和构成投资组合问题的多个约束的投资决策中的多目标投资组合优化的方法,所述方法包括:基于竞争目标执行第一多目标优化过程 ,以产生可能的解决方案的有效前沿; 观察生成的有效边界; 根据观察,确定存在差距的有效边界的一个区域; 并在差距填补过程中,有效的前沿在差距领域得到补充,有效的前沿被用于投资决策。

    Performance enhancement of optimization processes
    8.
    发明授权
    Performance enhancement of optimization processes 有权
    优化过程的性能提升

    公开(公告)号:US07457786B2

    公开(公告)日:2008-11-25

    申请号:US11210120

    申请日:2005-08-23

    CPC分类号: G06N3/126

    摘要: The performance of optimization algorithms operating with compute-intensive fitness functions is enhanced by constraining time-intensive fitness evaluations for candidate solutions that show low likelihood of being fit at early stages of the fitness evaluation. By prematurely discarding alternatives that could be potentially optimal upon complete fitness evaluation but with low likelihood, the running time of the overall optimization process is advantageously reduced substantially, thereby trading off time complexity for search fidelity.

    摘要翻译: 运用计算密集型健身功能的优化算法的性能通过约束对健身评估的早期阶段适合的可能性较低的候选解决方案的时间密集型健身评估来加强。 通过过早地丢弃可能在完全健身评估中可能是最佳的但具有低可能性的替代方案,有利地大大降低了整体优化过程的运行时间,从而消除了搜索保真度的时间复杂性。

    Method and system for efficient data collection and storage
    9.
    发明授权
    Method and system for efficient data collection and storage 有权
    高效数据采集和存储的方法和系统

    公开(公告)号:US08116936B2

    公开(公告)日:2012-02-14

    申请号:US11860626

    申请日:2007-09-25

    IPC分类号: F02D45/00

    摘要: A system for collecting and storing performance data for an engine is provided. The system includes one or more sensors configured to generate sensor data signals representative of one or more engine data performance parameters. The system further includes a data sampling component, a data quantizing component, a data storage sampling rate component, a data encoding component and a data storage component. The data sampling component is configured to sample the sensor data signals at a data sampling rate. The data quantizing component is configured to generate quantized data samples corresponding to the sampled sensor data signals. The data storage sampling rate component is configured to determine a data storage sampling rate for the quantized data samples, based on an analysis of at least a subset of the quantized data samples. The data encoding component is configured to encode the quantized data samples according to the data storage sampling rate, and the data storage component is configured to store the encoded data samples from the encoding component.

    摘要翻译: 提供了一种用于收集和存储发动机性能数据的系统。 该系统包括配置成产生代表一个或多个引擎数据性能参数的传感器数据信号的一个或多个传感器。 该系统还包括数据采样组件,数据量化组件,数据存储采样率组件,数据编码组件和数据存储组件。 数据采样组件被配置为以数据采样率对传感器数据信号进行采样。 数据量化部件被配置为产生对应于采样的传感器数据信号的量化数据采样。 数据存储采样速率分量被配置为基于对量化数据样本的至少一个子集的分析来确定量化数据采样的数据存储采样率。 数据编码部件被配置为根据数据存储采样率对量化的数据样本进行编码,并且数据存储部件被配置为存储来自编码部件的编码数据样本。

    System and process for a fusion classification for insurance underwriting suitable for use by an automated system
    10.
    发明授权
    System and process for a fusion classification for insurance underwriting suitable for use by an automated system 有权
    用于融合分类的系统和过程,适用于自动化系统使用的保险承保

    公开(公告)号:US07383239B2

    公开(公告)日:2008-06-03

    申请号:US10425721

    申请日:2003-04-30

    IPC分类号: G06F17/00 G06N5/02

    CPC分类号: G06Q40/08 G06Q40/00

    摘要: A method and system for fusing a collection of classifiers used for an automated insurance underwriting system and/or its quality assurance is described. Specifically, the outputs of a collection of classifiers are fused. The fusion of the data will typically result in some amount of consensus and some amount of conflict among the classifiers. The consensus will be measured and used to estimate a degree of confidence in the fused decisions. Based on the decision and degree of confidence of the fusion and the decision and degree of confidence of the production decision engine, a comparison module may then be used to identify cases for audit, cases for augmenting the training/test sets for re-tuning production decision engine, cases for review, or may simply trigger a record of its occurrence for tracking purposes. The fusion can compensate for the potential correlation among the classifiers. The reliability of each classifier can be represented by a static or dynamic discounting factor, which will reflect the expected accuracy of the classifier. A static discounting factor is used to represent a prior expectation about the classifier's reliability, e.g., it might be based on the average past accuracy of the model, while a dynamic discounting is used to represent a conditional assessment of the classifier's reliability, e.g., whenever a classifier bases its output on an insufficient number of points it is not reliable.

    摘要翻译: 描述用于融合用于自动保险承保系统的分类器集合和/或其质量保证的方法和系统。 具体来说,分类器的集合的输出被融合。 数据的融合通常会导致一些共识和分类器之间的一些冲突。 共识将被测量并用于估计融合决策的信心程度。 根据融合的决定和信心程度以及生产​​决策引擎的决策和决策程度,然后可以使用比较模块来识别审计案例,增加用于重新调整生产的培训/测试集的案例 决策引擎,审查案例,或者可以简单地触发其发生记录以进行跟踪。 融合可以补偿分类器之间的潜在相关性。 每个分类器的可靠性可以由静态或动态折扣因子表示,这将反映分类器的预期准确性。 静态折扣因子用于表示对分类器的可靠性的先前期望,例如,可以基于模型的平均过去精度,而使用动态贴现来表示分类器的可靠性的条件评估,例如,每当 分类器的输出基于不可靠的点数不足。