公开(公告)号：US20240402689A1

公开(公告)日：2024-12-05

申请号：US18203771

申请日：2023-05-31

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Dmitriy ITKIS ,  Matthew T. GERDES ,  Kenny C. GROSS ,  Guang Chao WANG

IPC: G05B19/418 ,  G06N20/00

Abstract: Systems, methods, and other embodiments associated with quadratic acceleration boost of compute performance for ML prognostics are described. In one embodiment, a prognostic acceleration method includes separating time series signals into a plurality of alternative configurations of clusters based on correlations between the time series signals. Machine learning models are trained for individual clusters in the alternative configurations of clusters. One or more of the alternative configurations of clusters is determined to be viable for use in a production environment based on whether the trained machine learning models for the individual clusters satisfy an accuracy threshold and a completion time threshold. Then, one configuration is selected from the alternative configurations of clusters that were determined to be viable configurations. Production machine learning models are deployed into the production environment to detect anomalies in the time series signals based on the selected configuration.

公开(公告)号：US20240344485A1

公开(公告)日：2024-10-17

申请号：US18133197

申请日：2023-04-11

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Dmitriy ITKIS ,  Guang Chao WANG ,  Ruixian LIU ,  Kenny C. GROSS

IPC: F02D41/22 ,  G06N20/20

CPC classification number: F02D41/22 ,  G06N20/20

Abstract: Systems, methods, and other embodiments associated with a merged-surface 3D fingerprint technique for improved prognostics for assets are described. In one embodiment, a method includes generating a set of time series signals from sensor readings of a reference device while the reference device is operated through multiple individual iterations of an exercise profile. The reference device operates in a known undegraded state. The method then separates the set of time series signals into segments that correspond to the individual iterations of the exercise profile. The method then aligns and merges the segments to generate a merged reference fingerprint. The method then trains a machine learning model to detect anomalous departures from the known undegraded state based on the merged reference fingerprint.