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    AUTOMATED AUGMENTED MODEL EXTENSION FOR ROBUST SYSTEM DESIGN
    12.
    发明申请
    AUTOMATED AUGMENTED MODEL EXTENSION FOR ROBUST SYSTEM DESIGN 审中-公开
    自动化的增强模型扩展用于稳健系统设计

    公开(公告)号：US20150051890A1

    公开(公告)日：2015-02-19

    申请号：US13967503

    申请日：2013-08-15

    申请人： Palo Alto Research Center Incorporated

    发明人： Bhaskar Saha ,  William C. Janssen, Jr. ,  Daniel G. Bobrow ,  Tolga Kurtoglu ,  Johan de Kleer

    IPC分类号： G06F17/50

    CPC分类号： G06F17/50 ,  G06F8/35 ,  G06F8/40 ,  G06F17/5009

    摘要： A method is provided for automatically generating an augmented model of a cyber-physical component. Cyber-physical components are built from or depend upon the synergy computational and physical components. The method includes: reading an input model (22) into a processor (10), the input model describing a nominal mode of operation for a physical component modeled by the input model; parsing with the processor the input model to generate a parse thereof; analyzing with the processor the parse of the input model; and automatically writing with the processor an augmented model (42) for the physical component from the input model based on the analysis, the augmented model describing the nominal mode of operation for the modeled physical component and at least one alternate mode of operation for the modeled physical component which is different from the nominal mode of operation. The augmented component models can be used to provide analyses of the behavior of faulted systems, including diagnosis, reliability, resiliency to faults and maintainability.

    摘要翻译： 提供了一种用于自动生成网络物理组件的增强模型的方法。 网络物理组件由协同计算和物理组件构建或取决于协同计算和物理组件。 该方法包括：将输入模型（22）读入处理器（10），所述输入模型描述由所述输入模型建模的物理分量的标称操作模式; 与处理器解析输入模型以生成其解析; 用处理器分析输入模型的解析; 并且基于所述分析，从所述输入模型自动地向所述处理器写入用于所述物理组件的增强模型（42），所述增强模型描述所建模的物理组件的标称操作模式以及所建模的物理组件的至少一个备选操作模式 不同于标称操作模式的物理组件。 增强的组件模型可用于提供故障系统的行为分析，包括诊断，可靠性，故障弹性和可维护性。

    System and method for operational-data-based detection of anomaly of a machine tool
    18.
    发明授权
    System and method for operational-data-based detection of anomaly of a machine tool 有权

    公开(公告)号：US11237539B2

    公开(公告)日：2022-02-01

    申请号：US16988477

    申请日：2020-08-07

    申请人： Palo Alto Research Center Incorporated

    发明人： Linxia Liao ,  Rajinderjeet Singh Minhas ,  Arvind Rangarajan ,  Tolga Kurtoglu ,  Johan de Kleer

    IPC分类号： G05B19/4065 ,  G05B23/02 ,  G01M13/00 ,  B23Q17/09

    摘要： A self-aware machine platform is implemented through analyzing operational data of machining tools to achieve machine tool damage assessment, prediction and planning in manufacturing shop floor. Machining processes are first identified by matching similar processes through an ICP algorithm. Machining processes are further clustered by Hotelling's T-squared statistics. Degradation of the machining tool is detected through a trend of the operational data within a cluster of machining processes by a monotonicity test, and the remaining useful life of the machining tool is predicted through a particle filter by extrapolating the trend under a first-order Markov process. In addition, process anomalies across machines are detected through a combination of outlier detection methods including SOMs, multivariate regression, and robust Mahalanobis distance. Warnings and recommendations are flexibly provided to manufacturing shop floor based on policy choice.

    System And Method For Operational-Data-Based Detection Of Anomaly Of A Machine Tool
    19.
    发明申请
    System And Method For Operational-Data-Based Detection Of Anomaly Of A Machine Tool 审中-公开

    公开(公告)号：US20200370996A1

    公开(公告)日：2020-11-26

    申请号：US16988477

    申请日：2020-08-07

    申请人： Palo Alto Research Center Incorporated

    发明人： Linxia Liao ,  Rajinderjeet Singh Minhas ,  Arvind Rangarajan ,  Tolga Kurtoglu ,  Johan de Kleer

    IPC分类号： G01M13/00 ,  B23Q17/09 ,  G05B19/4065 ,  G05B23/02

    摘要： A self-aware machine platform is implemented through analyzing operational data of machining tools to achieve machine tool damage assessment, prediction and planning in manufacturing shop floor. Machining processes are first identified by matching similar processes through an ICP algorithm. Machining processes are further clustered by Hotelling's T-squared statistics. Degradation of the machining tool is detected through a trend of the operational data within a cluster of machining processes by a monotonicity test, and the remaining useful life of the machining tool is predicted through a particle filter by extrapolating the trend under a first-order Markov process. In addition, process anomalies across machines are detected through a combination of outlier detection methods including SOMs, multivariate regression, and robust Mahalanobis distance. Warnings and recommendations are flexibly provided to manufacturing shop floor based on policy choice.

    System and method for detecting anomaly of a machine tool
    20.
    发明授权
    System and method for detecting anomaly of a machine tool 有权

    公开(公告)号：US10739230B2

    公开(公告)日：2020-08-11

    申请号：US16200014

    申请日：2018-11-26

    申请人： Palo Alto Research Center Incorporated

    发明人： Linxia Liao ,  Rajinderjeet Singh Minhas ,  Arvind Rangarajan ,  Tolga Kurtoglu ,  Johan de Kleer

    IPC分类号： G01M13/00 ,  G05B19/4065 ,  B23Q17/09 ,  G05B23/02

    摘要： A self-aware machine platform is implemented through analyzing operational data of machining tools to achieve machine tool damage assessment, prediction and planning in manufacturing shop floor. Machining processes are first identified by matching similar processes through an ICP algorithm. Machining processes are further clustered by Hotelling's T-squared statistics. Degradation of the machining tool is detected through a trend of the operational data within a cluster of machining processes by a monotonicity test, and the remaining useful life of the machining tool is predicted through a particle filter by extrapolating the trend under a first-order Markov process. In addition, process anomalies across machines are detected through a combination of outlier detection methods including SOMs, multivariate regression, and robust Mahalanobis distance. Warnings and recommendations are flexibly provided to manufacturing shop floor based on policy choice.