Invention Application
- Patent Title: USING A DOUBLE-BLIND CHALLENGE TO EVALUATE MACHINE-LEARNING-BASED PROGNOSTIC-SURVEILLANCE TECHNIQUES
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Application No.: US17090151Application Date: 2020-11-05
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Publication No.: US20220138090A1Publication Date: 2022-05-05
- Inventor: Rui Zhong , Guang C. Wang , Kenny C. Gross , Ashin George , Zexi Chen
- 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
- Main IPC: G06F11/36
- IPC: G06F11/36 ; G06N20/00 ; G06F21/60

Abstract:
A double-blind comparison is performed between prognostic-surveillance systems, which are located on a local system and a remote system. During operation, the local system inserts random faults into a dataset to produce a locally seeded dataset, wherein the random faults are inserted into random signals at random times with variable fault signatures. Next, the local system exchanges the locally seeded dataset with a remote system, and in return receives a remotely seeded dataset, which was produced by the remote system by inserting different random faults into the same dataset. Next, the local system uses a local prognostic-surveillance system to analyze the remotely seeded dataset to produce locally detected faults. Finally, the local system determines a performance of the local prognostic-surveillance system by comparing the locally detected faults against actual faults in the remotely seeded fault information. The remote system similarly determines a performance of a remote prognostic-surveillance system.
Public/Granted literature
- US12038830B2 Using a double-blind challenge to evaluate machine-learning-based prognostic-surveillance techniques Public/Granted day:2024-07-16
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