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    • 2. 发明授权
    • Recommendations for time to replace parts on machines
    • 建议时间更换机器上的零件
    • US09218694B1
    • 2015-12-22
    • US13964277
    • 2013-08-12
    • THE BOEING COMPANY
    • Oscar KipersztokUri NodelmanMichael Swayne
    • G07C5/00G05B23/00G05B23/02G06Q10/00
    • G07C5/006G05B23/0283G05B2219/2637G06Q10/20G07C5/0808
    • Systems and methods of recommending replacement of parts on machines. In one embodiment, a method of recommending replacement includes receiving data for a part type, determining a cumulative probability function for an infant mortality of the part type, and determining a cumulative probability function for a natural life of the part type. The method includes defining a lower time boundary and an upper time boundary between which the part type is considered operative. The lower time boundary is defined at a time point at an intersection between the cumulative probability function for the infant mortality and the cumulative probability function for the natural life of the part type. The upper time boundary is defined at a time point representing an estimated end of an operative life of the part type. Recommending replacement of a part on a machine may then be determined based on the upper and lower time boundaries.
    • 在机器上推荐更换零件的系统和方法。 在一个实施例中,推荐替换的方法包括接收部件类型的数据,确定部件类型的婴儿死亡率的累积概率函数,以及确定部件类型的自然寿命的累积概率函数。 该方法包括定义较低的时间边界和其中部分类型被认为是有效的高时间边界。 在婴儿死亡率的累积概率函数与部分类型的自然生活的累积概率函数之间的交点处的时间点定义较低时间边界。 在表示部件类型的使用寿命的估计结束的时间点定义了较高时间边界。 然后可以基于上下时间边界来确定推荐更换机器上的零件。
    • 5. 发明申请
    • SYSTEMS, METHODS, AND COMPUTER PROGRAM PRODUCTS FOR GENERATING A QUERY SPECIFIC BAYESIAN NETWORK
    • 用于生成特定拜耳网络的系统,方法和计算机程序产品
    • US20170024652A1
    • 2017-01-26
    • US14809179
    • 2015-07-25
    • The Boeing Company
    • Oscar KipersztokUri Nodelman
    • G06N5/04G06N7/00
    • G06N7/005
    • Provided are improved systems, methods, and computer programs to facilitate predictive accuracy for strategic decision support using a query specific Bayesian network. An unconstrained domain model is defined by domain concepts and causal relationships between the domain concepts. Each causal relationship includes a value for the weight of causal belief for the causal relationship. In response to a query, the unconstrained domain model is transformed into a query specific Bayesian network for the domain model by identifying one or more cycles in the unconstrained domain model, eliminating the one or more cycles from the unconstrained domain model; identifying a sub-graph of the unconstrained domain model that is relevant to a query and creating one or more conditional probability tables that comprise the query specific Bayesian network.
    • 提供改进的系统,方法和计算机程序,以便利用特定于查询的贝叶斯网络来进行战略决策支持的预测准确性。 无约束域模型由域概念和领域概念之间的因果关系定义。 每个因果关系包括因果关系的因果信念的​​重要价值。 响应于查询,通过在非约束域模型中识别一个或多个周期,将无约束域模型转换为针对域模型的查询特定贝叶斯网络,从无约束域模型中消除一个或多个周期; 识别与查询相关的无约束域模型的子图,并创建构成查询特定贝叶斯网络的一个或多个条件概率表。