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    Diagnostic system with learning capabilities
    3.
    发明授权
    Diagnostic system with learning capabilities 失效
    具有学习能力的诊断系统

    公开(公告)号：US06442542B1

    公开(公告)日：2002-08-27

    申请号：US09415408

    申请日：1999-10-08

    申请人： Vipin Kewal Ramani ,  Rasiklal Punjalal Shah ,  Ramesh Ramachandran ,  Piero Patrone Bonissone ,  Yu-To Chen ,  Phillip Edward Steen ,  John Andrew Johnson

    发明人： Vipin Kewal Ramani ,  Rasiklal Punjalal Shah ,  Ramesh Ramachandran ,  Piero Patrone Bonissone ,  Yu-To Chen ,  Phillip Edward Steen ,  John Andrew Johnson

    IPC分类号： G06F1730

    CPC分类号： G01R31/2846 ,  G06F11/2257 ,  Y10S707/915 ,  Y10S707/99933 ,  Y10S707/99945

    摘要： A diagnostic system is provided for identifying faults in a machine (e.g., CT scanner, MRI system, x-ray apparatus) by analyzing a data file generated thereby. The diagnostic system includes a trained database containing a plurality of trained data, each trained data associated with one of plurality of known fault types. Each trained data is represented by a trained set of feature values and corresponding weight values. Once a data file is generated by the machine, a current set of feature values are extracted from the data file by performing various analyses (e.g., time domain analysis, frequency domain analysis, wavelet analysis). The current set of feature values extracted is analyzed by a fault detector which produces a candidate set of faults based on the trained set of feature values and corresponding weight values for each of the fault types. The candidate set of faults produced by the fault detector is presented to a user along with a recommend repair procedure. In cases where no fault is identified or in response to a misdiagnosis produced by the diagnostic system, the user may interactively input a faulty condition associated with the machine being diagnosed (e.g., based on his/her experience). The diagnostic system further includes a learning subsystem which automatically updates the plurality of trained data based on the faulty condition input by the user.

    摘要翻译： 提供诊断系统，用于通过分析由此产生的数据文件来识别机器中的故障（例如，CT扫描仪，MRI系统，x射线设备）。 诊断系统包括训练数据库，其包含多个训练数据，每个训练数据与多个已知故障类型之一相关联。 每个经过训练的数据由经过训练的特征值组和对应的权重值表示。 一旦数据文件由机器生成，通过执行各种分析（例如，时域分析，频域分析，小波分析）从数据文件中提取当前的一组特征值。 提取的当前特征值组由故障检测器分析，故障检测器基于经过训练的特征值集合和每个故障类型的对应权重值产生候选的故障集合。 由故障检测器产生的候选故障集与推荐的修复过程一起呈现给用户。 在没有发现故障或者响应诊断系统产生的误诊的情况下，用户可以交互地输入与被诊断的机器相关联的故障状况（例如，基于他/她的经验）。 诊断系统还包括学习子系统，该学习子系统基于用户输入的故障条件自动更新多个经过训练的数据。

    Complex system diagnostic service model selection method and apparatus
    5.
    发明授权
    Complex system diagnostic service model selection method and apparatus 有权
    复杂系统诊断服务模型选择方法和装置

    公开(公告)号：US07254747B2

    公开(公告)日：2007-08-07

    申请号：US10402874

    申请日：2003-03-28

    申请人： Mark David Osborn ,  Rasiklal Punjalal Shah ,  Jieqian Cathy Chen

    发明人： Mark David Osborn ,  Rasiklal Punjalal Shah ,  Jieqian Cathy Chen

    IPC分类号： G06F11/00

    CPC分类号： G06F11/079 ,  G05B23/0278 ,  G06F11/0727 ,  G06F11/0793

    摘要： A technique is provided for selecting among a plurality of service models for addressing serviceable events and faults in a complex machine system. Indicators established for each of the models serve as the basis for comparison to input data acquired from the complex system or from manual or semi-automated data acquisition methods. Based upon the comparisons, and upon flexible selection criteria, one or more of the service models is selected to most efficiently address the most likely root cause of the serviceable event or fault.

    摘要翻译： 提供了一种用于在复杂机器系统中的多个服务模型之间进行选择以寻址可服务事件和故障的技术。 为每个模型建立的指标作为与从复杂系统或手动或半自动数据采集方法获取的输入数据进行比较的基础。 基于比较，并且根据灵活的选择标准，选择一个或多个服务模型以最有效地解决可服务事件或故障的最可能的根本原因。

    System and method for predicting component failures in large systems
    9.
    发明授权
    System and method for predicting component failures in large systems 有权
    用于预测大型系统中组件故障的系统和方法

    公开(公告)号：US07103509B2

    公开(公告)日：2006-09-05

    申请号：US10995981

    申请日：2004-11-23

    申请人： Rasiklal Punjalal Shah ,  Vrinda Rajiv ,  Mark David Osborn ,  Mahesh Kumar Asati ,  Piero Patrone Bonissone

    发明人： Rasiklal Punjalal Shah ,  Vrinda Rajiv ,  Mark David Osborn ,  Mahesh Kumar Asati ,  Piero Patrone Bonissone

    IPC分类号： G06F15/00

    CPC分类号： G01D3/08 ,  G01D1/00

    摘要： A method for predicting a time to failure of a component in a system is presented. The method comprises obtaining a set of data measurements related to the component. The set of data measurements are representative of a plurality of parameters including a plurality of leading parameters. The method comprises generating a prediction model based upon the leading parameters considered in combination. The prediction model is then used to predict the time to failure of the component based on a set of real-time measurements, wherein the plurality of parameters are processed to predict the time to failure for the component. Finally, a confidence level for the predicted time to failure is determined based upon the plurality of parameters.

    摘要翻译： 提出了一种用于预测系统中组件故障时间的方法。 该方法包括获得与该组件相关的一组数据测量。 数据测量集合代表包括多个前导参数的多个参数。 该方法包括基于组合考虑的前导参数生成预测模型。 然后，预测模型用于基于一组实时测量来预测组件的故障时间，其中处理多个参数以预测组件的故障时间。 最后，基于多个参数确定预测的故障时间的置信水平。