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公开(公告)号：US20190205751A1
公开(公告)日：2019-07-04
申请号：US16235355
申请日：2018-12-28
Applicant: University of Southern California , Chevron U.S.A. Inc.
Inventor： CHUNGMING CHEUNG , PALASH GOYAL , ARASH SABER TEHRANI , VIKTOR K. PRASANNA , LISA ANN BRENSKELLE
CPC classification number: G06N3/08 , E21B47/00 , E21B2041/0028 , G06F17/18 , G06N3/084 , G06N5/046 , G06N20/10
Abstract: A computer-implemented method for prioritizing candidate objects on which to perform a physical process includes receiving a time series history of measurements from each of a plurality of candidate objects at a data processing framework. The method further includes reducing dimensionality of the time series history of measurements with a convolutional autoencoder to obtain latent features for each of the plurality of candidate objects. The method also includes applying a kernel regression model to the latent features to generate a predicted value of physical output for performing the physical process on each of the plurality of candidate objects. The method additionally includes generating a prioritization of the candidate objects based on the values of physical output. The method involves selecting fewer than all of the plurality of candidate objects on which to perform the physical process. The selected candidate objects are based on the prioritization.
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公开(公告)号：US20190205360A1
公开(公告)日：2019-07-04
申请号：US16235387
申请日：2018-12-28
Applicant: University of Southern California , Chevron U.S.A. Inc.
Inventor： CHUNGMING CHEUNG , PALASH GOYAL , ARASH SABER TEHRANI , VIKTOR K. PRASANNA , LISA ANN BRENSKELLE
CPC classification number: G06N3/08 , E21B47/00 , E21B2041/0028 , G06F17/18 , G06N3/084 , G06N5/046 , G06N20/10
Abstract: A computer-implemented method for prioritizing candidate objects on which to perform a physical process includes receiving a time series history of measurements from each of a plurality of candidate objects at a data processing framework. The method further includes reducing dimensionality of the time series history of measurements with a convolutional autoencoder to obtain latent features for each of the plurality of candidate objects. The method also includes applying a kernel regression model to the latent features to generate a predicted value of physical output for performing the physical process on each of the plurality of candidate objects. The method additionally includes generating a prioritization of the candidate objects based on the values of physical output. The method involves selecting fewer than all of the plurality of candidate objects on which to perform the physical process. The selected candidate objects are based on the prioritization.
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公开(公告)号：US20180136019A1
公开(公告)日：2018-05-17
申请号：US15811477
申请日：2017-11-13
Applicant: Chevron U.S.A. Inc. , University of Southern California
Inventor： ALISHA DESHPANDE , SI-ZHAO J. QIN , LISA ANN BRENSKELLE
CPC classification number: G01D18/002 , G01D3/08 , G06F17/16 , G06F17/18 , G06K9/00523 , G06K9/6247
Abstract: Methods and systems for detecting a fault in a data set from an industrial process are disclosed. One method includes forming a first data matrix at a data processing framework from time-series training data, and performing a principal component pursuit on the first data matrix to form an uncorrupted, unscaled matrix and a sparse matrix in the memory, and scaling the uncorrupted, unscaled matrix to form an uncorrupted scaled matrix. The method also includes performing a dynamic principal component analysis (DPCA) on the uncorrupted scaled matrix to form a DPCA model, and determining a squared prediction error from the DPCA model. Based on the squared prediction error, faults are detected in a different data set from operation of the industrial process. At least one of (1) correcting the one or more faults in the different data set or (2) performing a repair operation on a sensor is performed.
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