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    Diagnostic system with learning capabilities
    2.
    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。

    System and method for predicting component failures in large systems
    5.
    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。

    System and method for generating a fuel-optimal reference velocity profile for a rail-based transportation handling controller
    7.
    发明授权
    System and method for generating a fuel-optimal reference velocity profile for a rail-based transportation handling controller 失效
    用于为基于轨道的运输处理控制器生成燃料最优参考速度分布的系统和方法

    公开(公告)号：US06243694B1

    公开(公告)日：2001-06-05

    申请号：US08999199

    申请日：1997-12-29

    申请人： Piero Patrone Bonissone ,  Yu-To Chen ,  Pratap Shankar Khedkar ,  Paul Kenneth Houpt ,  John Lewis Schneiter

    发明人： Piero Patrone Bonissone ,  Yu-To Chen ,  Pratap Shankar Khedkar ,  Paul Kenneth Houpt ,  John Lewis Schneiter

    IPC分类号： H61F214

    CPC分类号： G05B13/0275 ,  G05B13/0265

    摘要： In this invention there is disclosed a system and method for generating a fuel-optimal reference velocity profile for a rail-based transportation handling controller. The fuel-optimal reference velocity profile is used to reduce the amount of fuel consumption. The fuel-optimal reference velocity profile is generated to take into account the speed that a train must travel at in order to satisfy a given schedule, speed constraints that will minimize fuel consumption, and recommended speeds that will prevent train breaks, derailments, cargo damage and violation of safety rules. In this invention, the fuel-optimal reference velocity profile is generated by using a genetic algorithm.

    摘要翻译： 在本发明中，公开了一种用于产生用于基于轨道的运输处理控制器的燃料最优参考速度分布的系统和方法。 燃料最佳参考速度曲线用于减少燃料消耗量。 产生燃料最佳参考速度曲线，以考虑列车必须行驶的速度，以满足给定的时间表，将最小化燃料消耗的速度限制以及将阻止列车断开，脱轨，货物损坏的推荐速度 违反安全规定。 在本发明中，通过使用遗传算法产生燃料最优参考速度分布。

    Paper web breakage prediction using principal components analysis and classification and regression trees
    9.
    发明授权
    Paper web breakage prediction using principal components analysis and classification and regression trees 失效
    纸网断裂预测采用主成分分析和分类回归树

    公开(公告)号：US06466877B1

    公开(公告)日：2002-10-15

    申请号：US09583151

    申请日：2000-05-30

    申请人： Yu-To Chen ,  Piero Patrone Bonissone

    发明人： Yu-To Chen ,  Piero Patrone Bonissone

    IPC分类号： G01N1908

    CPC分类号： G01N33/346

    摘要： A system and method for predicting web breaks in a paper machine. Principal components analysis (PCA) and classification and regression tree (CART) modeling are used to predict web break sensitivity from sensor measurements taken from the paper machine. Also, the CART model is used to isolate the root cause of the predicted web break sensitivity.

    摘要翻译： 一种用于预测造纸机中的卷筒纸断裂的系统和方法。 主成分分析（PCA）和分类和回归树（CART）建模用于从造纸机的传感器测量中预测卷筒纸断裂敏感性。 此外，CART模型用于隔离预测的Web Break灵敏度的根本原因。

    System and method for paper web time-break prediction
    10.
    发明授权
    System and method for paper web time-break prediction 失效
    纸幅时间戳预测的系统和方法

    公开(公告)号：US06405140B1

    公开(公告)日：2002-06-11

    申请号：US09583155

    申请日：2000-05-30

    申请人： Yu-To Chen ,  Piero Patrone Bonissone

    发明人： Yu-To Chen ,  Piero Patrone Bonissone

    IPC分类号： G01B530

    CPC分类号： D21G9/0009

    摘要： A system and method for generating a time-to-break prediction for a paper web in a paper machine. This invention uses principal components analysis, neuro-fuzzy systems and trending analysis to form a model for predicting the time-to-break of the paper web from sensor measurements of paper machine process variables. The model is used to isolate the root cause of the predicted web break.

    摘要翻译： 一种用于在造纸机中生成纸幅的时间间隔预测的系统和方法。 本发明使用主成分分析，神经模糊系统和趋势分析，形成一个模型，用于通过纸机过程变量的传感器测量来预测纸幅的断裂时间。 该模型用于隔离预测的Web Break的根本原因。