Invention Grant
- Patent Title: Hybrid clustering-partitioning techniques that optimizes accuracy and compute cost for prognostic surveillance of sensor data
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Application No.: US15793742Application Date: 2017-10-25
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Publication No.: US10452510B2Publication Date: 2019-10-22
- Inventor: Kenny C. Gross , Mengying Li , Alan Paul Wood
- Applicant: Oracle International Corporation
- Applicant Address: US CA Redwood Shores
- Assignee: Oracle International Corporation
- Current Assignee: Oracle International Corporation
- Current Assignee Address: US CA Redwood Shores
- Agency: Park, Vaughan, Fleming & Dowler LLP
- Main IPC: G06F11/30
- IPC: G06F11/30 ; G06F11/00 ; G06F21/55 ; G06N7/00 ; G06F17/18 ; G06N3/08 ; G06N20/00

Abstract:
The disclosed embodiments relate to a system for performing prognostic surveillance operations on sensor data. During operation, the system obtains a group of signals from sensors in a monitored system during operation of the monitored system. Next, if possible, the system performs a clustering operation, which divides the group of signals into groups of correlated signals. Then, for one or more groups of signals that exceed a specified size, the system randomly partitions the groups of signals into smaller groups of signals. Next, for each group of signals, the system trains an inferential model for a prognostic pattern-recognition system based on signals in the group of signals. Then, for each group of signals, the system uses a prognostic pattern-recognition system in a surveillance mode and the inferential model to detect incipient anomalies that arise during execution of the monitored system.
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