公开(公告)号：US11928565B2

公开(公告)日：2024-03-12

申请号：US18049149

申请日：2022-10-24

Applicant: Chevron U.S.A. Inc. ,  University of Southern California

Inventor： Yining Dong ,  Alisha Deshpande ,  Yingying Zheng ,  Lisa Ann Brenskelle ,  Si-Zhao Qin

IPC: G06N20/00 ,  G05B19/406 ,  G05B19/408 ,  G05B19/418 ,  G06F9/445 ,  G06F9/455 ,  G06F9/54 ,  G06F18/2135 ,  G06F18/23 ,  G06N7/00

CPC classification number: G06N20/00 ,  G05B19/406 ,  G05B19/408 ,  G05B19/41885 ,  G06F9/542 ,  G06F18/2135 ,  G06F18/23

Abstract: Methods and systems for building and maintaining model(s) of a physical process are disclosed. One method includes receiving training data associated with a plurality of different data sources, and performing a clustering process to form one or more clusters. For each of the one or more clusters, the method includes building a data model based on the training data associated with the data sources in the cluster, automatically performing a data cleansing process on operational data based on the data model, and automatically updating the data model based on updated training data that is received as operational data. For data sources excluded from the clusters, automatic building, data cleansing, and updating of models can also be applied.

公开(公告)号：US20160179599A1

公开(公告)日：2016-06-23

申请号：US14937701

申请日：2015-11-10

Applicant: Chevron U.S.A. Inc. ,  University of Southern California

Inventor： Alisha Deshpande ,  Yining Dong ,  Gang Li ,  Yingying Zheng ,  Si-Zhao Qin ,  Lisa Ann Brenskelle

IPC: G06F11/07 ,  H04L29/06

CPC classification number: H04L65/601 ,  G05B23/024 ,  G06F16/215 ,  H04L65/4069

Abstract: A computer-implemented method for reconstructing data includes receiving a selection of one or more input data streams at a data processing framework. The method can include determining existence of a fault in the input data stream(s). This determination can be based on receiving a definition of one or more analytics components at the data processing framework and applying a dynamic principal component analysis (DPCA) to the input data streams. Detection of the fault can be based at least in part on a prediction error and a variation in principal component subspace generated based on the DPCA. Detection of the fault can also be based on performing a wavelet transform to generate a set of coefficients defining the data stream, the set of coefficients including one or more coefficients representing a high frequency portion of data included in the data stream. The method can include reconstructing data at the fault.

Abstract translation: 用于重建数据的计算机实现的方法包括在数据处理框架处接收一个或多个输入数据流的选择。 该方法可以包括确定输入数据流中的故障的存在。 该确定可以基于在数据处理框架处接收一个或多个分析组件的定义并且将动态主成分分析（DPCA）应用于输入数据流。 故障的检测至少部分可以基于DPCA生成的预测误差和主成分子空间的变化。 故障的检测还可以基于执行小波变换以产生定义数据流的系数集合，所述系数集合包括表示数据流中包括的数据的高频部分的一个或多个系数。 该方法可以包括重建故障中的数据。

公开(公告)号：US20180267503A1

公开(公告)日：2018-09-20

申请号：US15926962

申请日：2018-03-20

Applicant: University of Southern California

Inventor： Si-Zhao Qin ,  Yining Dong

IPC: G05B19/408 ,  G05B19/418 ,  G05B13/04

CPC classification number: G05B19/408 ,  G05B13/04 ,  G05B19/418 ,  G05B23/0224 ,  G05B2219/31093 ,  G06F17/11 ,  G06K9/00 ,  Y02P90/02

Abstract: A method for extracting a set of principal time series data of dynamic latent variables. The method includes detecting, by a plurality of sensors, dynamic samples of data each corresponding to one of a plurality of original variables. The method also includes analyzing, using a controller, the dynamic samples of data to determine a plurality of latent variables that represent variation in the dynamic samples of data. The method also includes selecting, by the controller, at least one inner latent variable that corresponds to at least one of the plurality of original variables. The method also includes estimating an estimated current value of the at least one inner latent variable based on previous values of the at least one inner latent variable

公开(公告)号：US11507069B2

公开(公告)日：2022-11-22

申请号：US16865081

申请日：2020-05-01

Applicant: Chevron U.S.A. Inc. ,  University of Southern California

Inventor： Yining Dong ,  Alisha Deshpande ,  Yingying Zheng ,  Lisa Ann Brenskelle ,  Si-Zhao Qin

IPC: G06F9/44 ,  G06F9/455 ,  G06F9/445 ,  G06F11/00 ,  G05B19/418 ,  G05B19/408 ,  G05B19/406 ,  G06F9/54 ,  G06K9/62 ,  G06N20/00

Abstract: Methods and systems for building and maintaining model(s) of a physical process are disclosed. One method includes receiving training data associated with a plurality of different data sources, and performing a clustering process to form one or more clusters. For each of the one or more clusters, the method includes building a data model based on the training data associated with the data sources in the cluster, automatically performing a data cleansing process on operational data based on the data model, and automatically updating the data model based on updated training data that is received as operational data. For data sources excluded from the clusters, automatic building, data cleansing, and updating of models can also be applied.

公开(公告)号：US10955818B2

公开(公告)日：2021-03-23

申请号：US15926962

申请日：2018-03-20

Applicant: University of Southern California

Inventor： Si-Zhao Qin ,  Yining Dong

IPC: G05B19/408 ,  G05B19/418 ,  G05B13/04 ,  G06K9/00 ,  G05B23/02 ,  G06F17/11

Abstract: A method for extracting a set of principal time series data of dynamic latent variables. The method includes detecting, by a plurality of sensors, dynamic samples of data each corresponding to one of a plurality of original variables. The method also includes analyzing, using a controller, the dynamic samples of data to determine a plurality of latent variables that represent variation in the dynamic samples of data. The method also includes selecting, by the controller, at least one inner latent variable that corresponds to at least one of the plurality of original variables. The method also includes estimating an estimated current value of the at least one inner latent variable based on previous values of the at least one inner latent variable.