Invention Grant
- Patent Title: Maximizing the operational range for training parameters while selecting training vectors for a machine-learning model
-
Application No.: US17090112Application Date: 2020-11-05
-
Publication No.: US11860974B2Publication Date: 2024-01-02
- Inventor: Guang C. Wang , Kenny C. Gross , Zexi Chen
- Applicant: Oracle International Corporation
- Applicant Address: US CA Redwood Shores
- Assignee: ORACLE INTERNATIONAL CORPORATION
- Current Assignee: ORACLE INTERNATIONAL CORPORATION
- Current Assignee Address: CA Redwood Shores
- Agency: Park, Vaughan, Fleming & Dowler LLP
- Main IPC: G06F18/00
- IPC: G06F18/00 ; G06F18/214 ; G06F11/30 ; H04L9/40 ; G06N5/04 ; G06F18/231 ; G06N7/01

Abstract:
A system is provided for training an inferential model based on selected training vectors. During operation, the system receives training data comprising observations for a set of time-series signals gathered from sensors in a monitored system during normal fault-free operation. Next, the system divides the observations into N subgroups comprising non-overlapping time windows of observations. The system then selects observations with a local minimum value and a local maximum value for all signals from each subgroup to be training vectors for the inferential model. Finally, the system trains the inferential model using the selected training vectors. Note that by selecting observations with local minimum and maximum values to be training vectors, the system maximizes an operational range for the training vectors, which reduces clipping in estimates subsequently produced by the inferential model and thereby reduces false alarms.
Public/Granted literature
Information query