公开(公告)号：US20100042872A1

公开(公告)日：2010-02-18

申请号：US12192700

申请日：2008-08-15

Applicant: John C. Colclough ,  Timothy J. Felke ,  George D. Hadden ,  David M. Kolbet ,  Randy Magnuson

Inventor： John C. Colclough ,  Timothy J. Felke ,  George D. Hadden ,  David M. Kolbet ,  Randy Magnuson

IPC: G06N5/02 ,  G06F11/07

CPC classification number: G06N5/02 ,  G05B23/0251

Abstract: A recursive mapping structure for diagnostic models is provided. A parent diagnostic model includes a first input module, a first output module, a first monitor module coupled to the first input and first output modules, and a first propagation module coupled between the first input and first output modules. A child diagnostic model includes a second input module, a second output module, a second monitor module coupled to the second input and second output modules, and a second propagation module coupled between the second input and second output modules, wherein the second monitor module is coupled to the first monitor module.

Abstract translation: 提供了诊断模型的递归映射结构。 父诊断模型包括耦合到第一输入和第一输出模块的第一输入模块，第一输出模块，第一监控模块以及耦合在第一输入模块和第一输出模块之间的第一传播模块。 儿童诊断模型包括第二输入模块，第二输出模块，耦合到第二输入和第二输出模块的第二监测模块，以及耦合在第二输入模块和第二输出模块之间的第二传播模块，其中第二监测模块是 耦合到第一监视器模块。

公开(公告)号：US20100042366A1

公开(公告)日：2010-02-18

申请号：US12192665

申请日：2008-08-15

Applicant: John C. Colclough ,  Timothy J. Felke ,  George D. Hadden ,  David M. Kolbet ,  Randy Magnuson

Inventor： John C. Colclough ,  Timothy J. Felke ,  George D. Hadden ,  David M. Kolbet ,  Randy Magnuson

IPC: G06F15/00

CPC classification number: G06F11/008 ,  G05B23/0283 ,  G06F11/3013 ,  G06F11/3072

Abstract: A distributed decision making architecture for embedded prognostics in a platform is provided. An estimation system determines at least one performance characteristic of at least one device in the platform. A supervisory system is in communication with, and superior to, the estimation system. The supervisory system calculates a prediction of remaining useful life (RUL) of the at least one device in the platform. The supervisory system is adapted for collecting the at least one performance characteristic from the estimation system to generate a performance estimate, and implementing at least one data-driven equation to match the at least one performance estimate against of at least one known degradation condition to generate the prediction of remaining useful life of the at least one device.

Abstract translation: 提供了平台中嵌入式预测的分布式决策架构。 估计系统确定平台中的至少一个设备的至少一个性能特征。 监督系统与评估系统通信并优于估计系统。 监控系统计算平台中至少一个设备的剩余使用寿命（RUL）的预测。 所述监控系统适于从所述估计系统收集所述至少一个性能特征以产生性能估计，以及实施至少一个数据驱动方程以将所述至少一个性能估计与至少一个已知的降级条件相匹配以产生 预测所述至少一个设备的剩余使用寿命。

公开(公告)号：US20100198610A1

公开(公告)日：2010-08-05

申请号：US12366472

申请日：2009-02-05

Applicant: Asif Khalak ,  Bradley John Barton ,  Randy Magnuson ,  Qingqiu Ginger Shao ,  David Michael Kolbet ,  C. Arthur Dins

Inventor： Asif Khalak ,  Bradley John Barton ,  Randy Magnuson ,  Qingqiu Ginger Shao ,  David Michael Kolbet ,  C. Arthur Dins

IPC: G06Q50/00 ,  G06F17/18

CPC classification number: G06Q50/22 ,  G06F19/00 ,  G16H50/20

Abstract: The present application relates to a method of splitting a fault condition including receiving evidence observations of a monitored system from monitors connected in a many-to-many relationship to the failure modes, generating a fault condition, computing a relative probability of failure for each failure mode in the fault condition. When there is more than one failure mode in the fault condition, the method includes computing a relative probability of each pair of failure modes in the fault condition, ranking the computed relative probabilities of the individual failure modes and the computed relative probabilities of the pairs of failure modes. If the highest ranked failure mode is a pair of failure modes, the fault condition is split based on the failure modes in the highest ranked pair of failure modes are split. If the highest ranked failure mode is an individual failure mode, a failure is isolated based on the ranking.

Abstract translation: 本申请涉及一种将故障条件分解的方法，包括从多对多关系的监视器接收监视系统的证据观察到故障模式，产生故障状况，计算每个故障的相对故障概率 模式处于故障状态。 当故障状态有多种故障模式时，该方法包括计算故障状态下每对故障模式的相对概率，对各个故障模式的计算相对概率进行排序，计算出相应故障模式的相对概率 故障模式。 如果排名最高的故障模式是一对故障模式，则基于最高排序失败模式的故障模式分为故障状态。 如果排名最高的故障模式是单个故障模式，则基于排名隔离故障。