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    METHOD AND SYSTEM FOR PREDICTING TURBOMACHINERY FAILURE EVENTS EMPLOYING GENETIC ALGORITHM
    2.
    发明申请
    METHOD AND SYSTEM FOR PREDICTING TURBOMACHINERY FAILURE EVENTS EMPLOYING GENETIC ALGORITHM 有权
    用于预测使用遗传算法的涡轮机故障事件的方法和系统

    公开(公告)号：US20090100293A1

    公开(公告)日：2009-04-16

    申请号：US11872732

    申请日：2007-10-16

    申请人： Christina A. LaComb ,  John A. Interrante ,  Thomas R. Kiehl ,  Deniz Senturk-Doganaksoy ,  Bethany K. Hoogs

    发明人： Christina A. LaComb ,  John A. Interrante ,  Thomas R. Kiehl ,  Deniz Senturk-Doganaksoy ,  Bethany K. Hoogs

    IPC分类号： G06F11/07 ,  G06F15/18

    CPC分类号： G05B23/0229

    摘要： A method for predicting or detecting an event in turbomachinery includes the steps of obtaining operational data from at least one machine and at least one peer machine. The operational data comprises a plurality of performance metrics. A genetic algorithm (GA) analyzes the operational data, and generates a plurality of clauses, which are used to characterize the operational data. The clauses are evaluated as being either “true” or “false”. A fitness function identifies a fitness value for each of the clauses. A perturbation is applied to selected clauses to create additional clauses, which are then added to the clauses group. The steps of applying a fitness function, selecting a plurality of clauses, and applying a perturbation can be repeated until a predetermined fitness value is reached. The selected clauses are then applied to the operational data from the machine to detect or predict a past, present or future event.

    摘要翻译： 一种用于在涡轮机械中预测或检测事件的方法包括从至少一个机器和至少一个对等机器获得操作数据的步骤。 操作数据包括多个性能度量。 遗传算法（GA）分析操作数据，并生成多个条款，用于表征操作数据。 这些条款被评估为“真实”或“假”。 适应度函数识别每个条款的适合度值。 将扰动应用于选定的子句以创建其他子句，然后将其添加到子句组。 可以重复应用适应度函数，选择多个子句和应用扰动的步骤，直到达到预定的适应值。 所选择的条款然后被应用于来自机器的操作数据以检测或预测过去，现在或将来的事件。

    Method and system for predicting turbomachinery failure events employing genetic algorithm
    3.
    发明授权
    Method and system for predicting turbomachinery failure events employing genetic algorithm 有权
    使用遗传算法预测涡轮机械故障事件的方法和系统

    公开(公告)号：US07627454B2

    公开(公告)日：2009-12-01

    申请号：US11872732

    申请日：2007-10-16

    申请人： Christina A. LaComb ,  John A. Interrante ,  Thomas R. Kiehl ,  Deniz Senturk-Doganaksoy ,  Bethany K. Hoogs

    发明人： Christina A. LaComb ,  John A. Interrante ,  Thomas R. Kiehl ,  Deniz Senturk-Doganaksoy ,  Bethany K. Hoogs

    IPC分类号： G06F11/07 ,  G06F11/30

    CPC分类号： G05B23/0229

    摘要： A method for predicting or detecting an event in turbomachinery includes the steps of obtaining operational data from at least one machine and at least one peer machine. The operational data comprises a plurality of performance metrics. A genetic algorithm (GA) analyzes the operational data, and generates a plurality of clauses, which are used to characterize the operational data. The clauses are evaluated as being either “true” or “false”. A fitness function identifies a fitness value for each of the clauses. A perturbation is applied to selected clauses to create additional clauses, which are then added to the clauses group. The steps of applying a fitness function, selecting a plurality of clauses, and applying a perturbation can be repeated until a predetermined fitness value is reached. The selected clauses are then applied to the operational data from the machine to detect or predict a past, present or future event.

    摘要翻译： 一种用于在涡轮机械中预测或检测事件的方法包括从至少一个机器和至少一个对等机器获得操作数据的步骤。 操作数据包括多个性能度量。 遗传算法（GA）分析操作数据，并生成多个条款，用于表征操作数据。 这些条款被评估为“真实”或“假”。 适应度函数识别每个条款的适合度值。 将扰动应用于选定的子句以创建其他子句，然后将其添加到子句组。 可以重复应用适应度函数，选择多个子句和应用扰动的步骤，直到达到预定的适应值。 所选择的条款然后被应用于来自机器的操作数据以检测或预测过去，现在或将来的事件。

    Techniques for performing business analysis based on incomplete and/or stage-based data
    4.
    发明授权
    Techniques for performing business analysis based on incomplete and/or stage-based data 有权
    基于不完整和/或基于阶段的数据执行业务分析的技术

    公开(公告)号：US07676390B2

    公开(公告)日：2010-03-09

    申请号：US10654738

    申请日：2003-09-04

    申请人： Deniz Senturk ,  Christina A. LaComb ,  Roger W. Hoerl ,  Snehil Gambhir ,  Peter A. Kalish

    发明人： Deniz Senturk ,  Christina A. LaComb ,  Roger W. Hoerl ,  Snehil Gambhir ,  Peter A. Kalish

    IPC分类号： G06Q90/00

    CPC分类号： G06Q10/10 ,  G06Q10/063 ,  G06Q30/0201

    摘要： Electrical data processing techniques are described for performing business analysis based on datasets that are incomplete (e.g., contain censored data) and/or based on datasets that are derived from a stage-based business operation. A first technique offsets the effects of error caused by the incomplete dataset by performing a trending operation followed by a de-trending operation. A second technique provides a model containing multiple sub-models, where the output of one sub-model serves as the input to another sub-model in recursive fashion. A third technique determines when a specified event is likely to occur with respect to a given asset by first discriminating whether the event is very unlikely to occur; if the asset does not meet this initial test, it is further processed by a second sub-model, which determines the probability that the specified event will occur for each of a specified series of time intervals.

    摘要翻译： 描述了用于基于不完整（例如，包含审查数据）和/或基于从基于阶段的业务操作导出的数据集的数据集执行业务分析的电气数据处理技术。 第一种技术通过执行趋势化操作，然后进行去趋势操作来抵消由不完整数据集引起的错误的影响。 第二种技术提供了包含多个子模型的模型，其中一个子模型的输出以递归的方式用作另一个子模型的输入。 第三种技术通过首先区分事件是否不大可能发生，确定指定事件何时相对于给定资产可能发生; 如果该资产不符合该初始测试，则其进一步由第二子模型进行处理，该第二子模型确定指定事件将针对指定的一系列时间间隔中的每一个发生的概率。