公开(公告)号：US20230008658A1

公开(公告)日：2023-01-12

申请号：US17368840

申请日：2021-07-07

Applicant: Oracle International Corporation

Inventor： Richard P. Sonderegger ,  Kenneth P. Baclawski ,  Guang C. Wang ,  Anna Chystiakova ,  Dieter Gawlick ,  Zhen Hua Liu ,  Kenny C. Gross

IPC: G05B23/02 ,  G06N20/00 ,  G05B13/02 ,  G05B15/02

Abstract: The disclosed embodiments relate to a system that automatically adapts a prognostic-surveillance system to account for aging phenomena in a monitored system. During operation, the prognostic-surveillance system is operated in a surveillance mode, wherein a trained inferential model is used to analyze time-series signals from the monitored system to detect incipient anomalies. During the surveillance mode, the system periodically calculates a reward/cost metric associated with updating the trained inferential model. When the reward/cost metric exceeds a threshold, the system swaps the trained inferential model with an updated inferential model, which is trained to account for aging phenomena in the monitored system.

公开(公告)号：US11948051B2

公开(公告)日：2024-04-02

申请号：US16826478

申请日：2020-03-23

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Edward R. Wetherbee ,  Kenneth P. Baclawski ,  Guang C. Wang ,  Kenny C. Gross ,  Anna Chystiakova ,  Dieter Gawlick ,  Zhen Hua Liu ,  Richard Paul Sonderegger

IPC: G06F16/00 ,  G05B23/02 ,  G06F17/16 ,  G06F17/18 ,  G06F30/27 ,  G06N20/00 ,  G06F111/10 ,  G06N3/08 ,  G06N20/10

CPC classification number: G06N20/00 ,  G05B23/024 ,  G06F17/16 ,  G06F17/18 ,  G06F30/27 ,  G06F2111/10 ,  G06N3/08 ,  G06N20/10

Abstract: In one embodiment, a method for auditing the results of a machine learning model includes: retrieving a set of state estimates for original time series data values from a database under audit; reversing the state estimation computation for each of the state estimates to produce reconstituted time series data values for each of the state estimates; retrieving the original time series data values from the database under audit; comparing the original time series data values pairwise with the reconstituted time series data values to determine whether the original time series and reconstituted time series match; and generating a signal that the database under audit (i) has not been modified where the original time series and reconstituted time series match, and (ii) has been modified where the original time series and reconstituted time series do not match.

公开(公告)号：US11797882B2

公开(公告)日：2023-10-24

申请号：US16691321

申请日：2019-11-21

Applicant: Oracle International Corporation

Inventor： Kenneth P. Baclawski ,  Dieter Gawlick ,  Kenny C. Gross ,  Zhen Hua Liu

IPC: G06N20/00 ,  G06F16/2458 ,  G06N7/01

CPC classification number: G06N20/00 ,  G06F16/2474 ,  G06N7/01

Abstract: We describe a system that performs prognostic-surveillance operations based on an inferential model that dynamically adapts to evolving operational characteristics of a monitored asset. During a surveillance mode, the system receives a set of time-series signals gathered from sensors in the monitored asset. Next, the system uses an inferential model to generate estimated values for the set of time-series signals, and then performs a pairwise differencing operation between actual values and the estimated values for the set of time-series signals to produce residuals. Next, the system performs a sequential probability ratio test (SPRT) on the residuals to produce SPRT alarms. When a tripping frequency of the SPRT alarms exceeds a threshold value, which is indicative of an incipient anomaly in the monitored asset, the system triggers an alert. While the prognostic-surveillance system is operating in the surveillance mode, the system incrementally updates the inferential model based on the time-series signals.

公开(公告)号：US11782429B2

公开(公告)日：2023-10-10

申请号：US17368840

申请日：2021-07-07

Applicant: Oracle International Corporation

Inventor： Richard P. Sonderegger ,  Kenneth P. Baclawski ,  Guang C. Wang ,  Anna Chystiakova ,  Dieter Gawlick ,  Zhen Hua Liu ,  Kenny C. Gross

IPC: G05B23/02 ,  G06N20/00 ,  G05B15/02 ,  G05B13/02

CPC classification number: G05B23/0221 ,  G05B13/0265 ,  G05B15/02 ,  G06N20/00 ,  G05B2223/02

Abstract: The disclosed embodiments relate to a system that automatically adapts a prognostic-surveillance system to account for aging phenomena in a monitored system. During operation, the prognostic-surveillance system is operated in a surveillance mode, wherein a trained inferential model is used to analyze time-series signals from the monitored system to detect incipient anomalies. During the surveillance mode, the system periodically calculates a reward/cost metric associated with updating the trained inferential model. When the reward/cost metric exceeds a threshold, the system swaps the trained inferential model with an updated inferential model, which is trained to account for aging phenomena in the monitored system.

公开(公告)号：US20230035541A1

公开(公告)日：2023-02-02

申请号：US17386965

申请日：2021-07-28

Applicant: Oracle International Corporation

Inventor： Menglin Liu ,  Richard P. Sonderegger ,  Kenneth P. Baclawski ,  Dieter Gawlick ,  Anna Chystiakova ,  Guang C. Wang ,  Zhen Hua Liu ,  Hariharan Balasubramanian ,  Kenny C. Gross

IPC: G06N7/00 ,  G06N5/04 ,  G06K9/62 ,  G06K9/00

Abstract: The disclosed embodiments relate to a system that optimizes a prognostic-surveillance system to achieve a user-selectable functional objective. During operation, the system allows a user to select a functional objective to be optimized from a set of functional objectives for the prognostic-surveillance system. Next, the system optimizes the selected functional objective by performing Monte Carlo simulations, which vary operational parameters for the prognostic-surveillance system while the prognostic-surveillance system operates on synthesized signals, to determine optimal values for the operational parameters that optimize the selected functional objective.

公开(公告)号：US20210158202A1

公开(公告)日：2021-05-27

申请号：US16691321

申请日：2019-11-21

Applicant: Oracle International Corporation

Inventor： Kenneth P. Baclawski ,  Dieter Gawlick ,  Kenny C. Gross ,  Zhen Hua Liu

IPC: G06N20/00 ,  G06F16/2458 ,  G06N7/00 ,  G06F16/28

Abstract: We describe a system that performs prognostic-surveillance operations based on an inferential model that dynamically adapts to evolving operational characteristics of a monitored asset. During a surveillance mode, the system receives a set of time-series signals gathered from sensors in the monitored asset. Next, the system uses an inferential model to generate estimated values for the set of time-series signals, and then performs a pairwise differencing operation between actual values and the estimated values for the set of time-series signals to produce residuals. Next, the system performs a sequential probability ratio test (SPRT) on the residuals to produce SPRT alarms. When a tripping frequency of the SPRT alarms exceeds a threshold value, which is indicative of an incipient anomaly in the monitored asset, the system triggers an alert. While the prognostic-surveillance system is operating in the surveillance mode, the system incrementally updates the inferential model based on the time-series signals.