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

IPC: G01D18/00 ,  G06F17/16 ,  G06F17/18

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.