公开(公告)号：US11500411B2

公开(公告)日：2022-11-15

申请号：US16052638

申请日：2018-08-02

Applicant: Oracle International Corporation

Inventor： Kenny C. Gross ,  Guang C. Wang ,  Steven T. Jeffreys ,  Alan Paul Wood ,  Coleen L. MacMillan

IPC: G06F1/02 ,  G06F17/18 ,  G06K9/00 ,  G06F16/2458 ,  G06F1/03

Abstract: The disclosed embodiments relate to a system that compactly stores time-series sensor signals. During operation, the system receives original time-series signals comprising sequences of observations obtained from sensors in a monitored system. Next, the system formulizes the original time-series sensor signals to produce a set of equations, which can be used to generate synthetic time-series signals having the same correlation structure and the same stochastic properties as the original time-series signals. Finally, the system stores the formulized time-series sensor signals in place of the original time-series sensor signals.

公开(公告)号：US20190243799A1

公开(公告)日：2019-08-08

申请号：US15887234

申请日：2018-02-02

Applicant: Oracle International Corporation

Inventor： Kenny C. Gross ,  Mengying Li ,  Alan Paul Wood ,  Steven T. Jeffreys ,  Avishkar Misra ,  Lawrence L. Fumagalli, JR.

IPC: G06F15/18 ,  G06F17/30 ,  H04W4/38 ,  G06F17/18 ,  G06F17/14 ,  G06K9/62 ,  G05B19/048

CPC classification number: G06N20/00 ,  G05B19/048 ,  G06F16/2474 ,  G06F17/14 ,  G06F17/18 ,  G06K9/6256 ,  H04W4/38

Abstract: The disclosed embodiments relate to a system that facilitates development of machine-learning techniques to perform prognostic-surveillance operations on time-series data from a monitored system, such as a power plant and associated power-distribution system. During operation, the system receives original time-series signals comprising sequences of observations obtained from sensors in the monitored system. Next, the system decomposes the original time-series signals into deterministic and stochastic components. The system then uses the deterministic and stochastic components to produce synthetic time-series signals, which are statistically indistinguishable from the original time-series signals. Finally, the system enables a developer to use the synthetic time-series signals to develop machine-learning (ML) techniques to perform prognostic-surveillance operations on subsequently received time-series signals from the monitored system.

公开(公告)号：US20190243407A1

公开(公告)日：2019-08-08

申请号：US16052638

申请日：2018-08-02

Applicant: Oracle International Corporation

Inventor： Kenny C. Gross ,  Guang C. Wang ,  Steven T. Jeffreys ,  Alan Paul Wood ,  Coleen L. MacMillan

IPC: G06F1/02 ,  G06F17/18

CPC classification number: G06F1/022 ,  G06F17/18

Abstract: The disclosed embodiments relate to a system that compactly stores time-series sensor signals. During operation, the system receives original time-series signals comprising sequences of observations obtained from sensors in a monitored system. Next, the system formulizes the original time-series sensor signals to produce a set of equations, which can be used to generate synthetic time-series signals having the same correlation structure and the same stochastic properties as the original time-series signals. Finally, the system stores the formulized time-series sensor signals in place of the original time-series sensor signals.