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LABEL RECTIFICATION AND CLASSIFICATION/PREDICTION FOR MULTIVARIATE TIME SERIES DATA

机译:多元时间序列数据的标签校正和分类/预测

摘要

A method of directional label rectification with adaptive graph for multivariate time-series data includes obtaining an input matrix containing sensor data and a first event matrix containing event data, identifying in the input matrix at least one feature pattern and a first corresponding time stamp, identifying in the first event matrix at least one fault signature and a second corresponding time stamp, if both a label matrix and an affinity matrix are known, then optimizing a weight matrix, else if both the label matrix and the weight matrix are known, then optimizing the affinity matrix, else optimizing the label matrix, creating a dynamically rectified event matrix by applying the label matrix, the affinity matrix, and the weight matrix to the first event matrix; and applying the dynamically rectified event matrix to forecast a future status of the asset. A system and computer-readable medium are disclosed.
机译:一种用于多变量时间序列数据的带有自适应图的方向性标签校正的方法,包括获得包含传感器数据的输入矩阵和包含事件数据的第一事件矩阵,在输入矩阵中标识至少一个特征模式和第一对应时间戳,标识在第一事件矩阵中,如果标签矩阵和亲和矩阵都已知,则至少一个故障签名和第二相应时间戳,然后优化权重矩阵,否则,如果标签矩阵和权重矩阵都已知,则优化亲和度矩阵,否则优化标签矩阵,通过将标签矩阵,亲和度矩阵和权重矩阵应用于第一事件矩阵来创建动态校正的事件矩阵;应用动态校正的事件矩阵来预测资产的未来状态。公开了一种系统和计算机可读介质。

著录项

  • 公开/公告号US2019163549A1

    专利类型

  • 公开/公告日2019-05-30

    原文格式PDF

  • 申请/专利权人 GENERAL ELECTRIC COMPANY;

    申请/专利号US201715827559

  • 发明设计人 HAO HUANG;XIAOQIAN WANG;

    申请日2017-11-30

  • 分类号G06F11/07;G06N99;

  • 国家 US

  • 入库时间 2022-08-21 12:07:03

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