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    MACHINE CONDITION MONITORING USING DISCONTINUITY DETECTION
    3.
    发明公开
    MACHINE CONDITION MONITORING USING DISCONTINUITY DETECTION 审中-公开
    与DISKONTINUITÄTSERKENNUNG机器状态监测

    公开(公告)号：EP2132609A1

    公开(公告)日：2009-12-16

    申请号：EP08727053.4

    申请日：2008-03-20

    申请人： Siemens Corporate Research, INC.

    发明人： NEUBAUER, Claus ,  YUAN, Chao

    IPC分类号： G05B23/02

    CPC分类号： G05B23/0232

    摘要： Condition signals of machines are observed and one or more discontinuities are detected in the condition signals. The discontinuities in the condition signals are compensated for (e.g., by applying a shifting factor to models of the signals) and trends of the compensated condition signals are determined. The trends are used to predict future fault conditions in machines. Kalman filters comprising observation models and evolution models are used to determine the trends. Discontinuity in observed signals is detected using hypothesis testing.

    ROBUST SENSOR CORRELATION ANALYSIS FOR MACHINE CONDITION MONITORING
    4.
    发明公开
    ROBUST SENSOR CORRELATION ANALYSIS FOR MACHINE CONDITION MONITORING 审中-公开
    耐用的传感相关分析机器状态监测

    公开(公告)号：EP1955119A1

    公开(公告)日：2008-08-13

    申请号：EP06838755.4

    申请日：2006-12-01

    申请人： SIEMENS CORPORATE RESEARCH, INC.

    发明人： YUAN, Chao ,  BALDERER, Christian ,  HUANG, Tzu Kuo ,  NEUBAUER, Claus

    IPC分类号： G05B23/02

    CPC分类号： G05B19/4065 ,  G05B23/024 ,  G05B2219/37508

    摘要： A method for monitoring machine conditions is based on machine learning through the use of a statistical model. A correlation coefficient is calculated using weights assigned to each sample that indicate the likelihood that that sample is an outlier. The resulting correlation coefficient is more robust against outliers. The calculation of the weight is based on the Mahalanobis distance from the sample to the sample mean. Additionally, hierarchical clustering is applied to intuitively reveal group information among sensors. By specifying a similarity threshold, the user can easily obtain desired clustering results.

    MACHINE CONDITION MONITORING USING PATTERN RULES
    6.
    发明公开
    MACHINE CONDITION MONITORING USING PATTERN RULES 审中-公开
    机器状态监测技术结构规则

    公开(公告)号：EP2135144A1

    公开(公告)日：2009-12-23

    申请号：EP08742146.7

    申请日：2008-03-20

    申请人： Siemens Corporate Research, INC. ,  Siemens Aktiengesellschaft

    发明人： HACKSTEIN, Holger ,  NEUBAUER, Claus ,  YUAN, Chao

    IPC分类号： G05B23/02

    摘要： Pattern rules are created by comparing a condition signal pattern to a plurality of known signal patterns and determining a machine condition pattern rule based at least in part on the comparison of the condition signal pattern to one of the plurality of known signal patterns. A matching score based on the comparison of the condition signal pattern to one of the plurality of known signal patterns as well as a signal pattern duration is determined. The machine condition pattern rule is then defined for nonparametric condition signal patterns as a multipartite threshold rule with a first threshold based on the determined matching score and a second threshold based on the determined signal duration. For parametric signal patterns, one or more parameters of the signal pattern are determined and the machine condition pattern rule is further defined with a third threshold based on the determined one or more parameters.

    SYSTEM, DEVICE, AND METHODS FOR UPDATING SYSTEM-MONITORING MODELS
    7.
    发明公开
    SYSTEM, DEVICE, AND METHODS FOR UPDATING SYSTEM-MONITORING MODELS 有权
    系统，用于更新系统设备和方法监测模型

    公开(公告)号：EP1782142A1

    公开(公告)日：2007-05-09

    申请号：EP05788929.7

    申请日：2005-08-25

    申请人： SIEMENS CORPORATE RESEARCH, INC.

    发明人： YUAN, Chao ,  NEUBAUER, Claus ,  CATALTEPE, Zehra ,  MCCORKLE, Wesley ,  BRUMMEL, Hans-Gerd ,  FANG, Ming

    IPC分类号： G05B23/02

    CPC分类号： G05B17/02 ,  G05B23/0243

    摘要： A system (102) for updating a plurality of monitoring models is provided. The system (102) includes a model association module (202) that, for each of a plurality of monitored systems (104a, 104b, 104c) determines, an association between a particular monitored system and at least one of a plurality of estimation models. Each estimation model is based upon one of a plurality of distinct sets of estimation properties, and each set uniquely corresponds to a particular estimation model. The system also includes an updating module (204) that updates at least one of the estimation properties and propagates the updated estimation properties to each estimation model that corresponds to a distinct set containing at least one estimation property that is updated. The system further includes a model modification module (206) that modifies each estimation model that corresponds to a distinct set containing at least one estimation property that is updated.