公开(公告)号：US10037024B2

公开(公告)日：2018-07-31

申请号：US14997854

申请日：2016-01-18

Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC

Inventor： Diana M Wegner ,  Jeffrey A. Abell ,  Michael A. Wincek

IPC: G05B19/425 ,  G05B11/42 ,  G05B13/02 ,  G05B19/21 ,  G05B19/33 ,  G05B19/39 ,  G05B19/418

CPC classification number: G05B19/425 ,  G05B11/42 ,  G05B13/0255 ,  G05B19/21 ,  G05B19/33 ,  G05B19/39 ,  G05B19/41875 ,  G05B2219/32194 ,  G05B2219/36219 ,  Y02P90/22

Abstract: An automated method for discovering features in a repeatable process includes measuring raw time series data during the process using sensors. The time series data describes multiple parameters of the process. The method includes receiving, via a first controller, the time series data from the sensors, and stochastically generating candidate features from the raw time series data using a logic block or blocks of the first controller. The candidate features are predictive of a quality of a work piece manufactured via the repeatable process. The method also includes determining, via a genetic or evolutionary programming module, which generated candidate features are most predictive of the quality of the work piece, and executing a control action with respect to the repeatable process via a second controller using the most predictive candidate features. A system includes the controllers, the programming module, and the sensors.

公开(公告)号：US20170205815A1

公开(公告)日：2017-07-20

申请号：US14997854

申请日：2016-01-18

Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC

Inventor： Diana M. Wegner ,  Jeffrey A. Abell ,  Michael A. Wincek

IPC: G05B19/425 ,  G05B13/02 ,  G05B19/39 ,  G05B11/42 ,  G05B19/21 ,  G05B19/33

CPC classification number: G05B19/425 ,  G05B11/42 ,  G05B13/0255 ,  G05B19/21 ,  G05B19/33 ,  G05B19/39 ,  G05B19/41875 ,  G05B2219/32194 ,  G05B2219/36219 ,  Y02P90/22

Abstract: An automated method for discovering features in a repeatable process includes measuring raw time series data during the process using sensors. The time series data describes multiple parameters of the process. The method includes receiving, via a first controller, the time series data from the sensors, and stochastically generating candidate features from the raw time series data using a logic block or blocks of the first controller. The candidate features are predictive of a quality of a work piece manufactured via the repeatable process. The method also includes determining, via a genetic or evolutionary programming module, which generated candidate features are most predictive of the quality of the work piece, and executing a control action with respect to the repeatable process via a second controller using the most predictive candidate features. A system includes the controllers, the programming module, and the sensors.