Invention Application
- Patent Title: CHARACTERIZING SUSCEPTIBILITY OF A MACHINE-LEARNING MODEL TO FOLLOW SIGNAL DEGRADATION AND EVALUATING POSSIBLE MITIGATION STRATEGIES
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Application No.: US17086855Application Date: 2020-11-02
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Publication No.: US20220138316A1Publication Date: 2022-05-05
- Inventor: Zexi Chen , Kenny C. Gross , Ashin George , Guang C. Wang
- 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: G06F21/55
- IPC: G06F21/55 ; G06N20/00 ; G06N5/04 ; G06F17/16

Abstract:
The disclosed embodiments relate to a system that characterizes susceptibility of an inferential model to follow signal degradation. During operation, the system receives a set of time-series signals associated with sensors in a monitored system during normal fault-free operation. Next, the system trains the inferential model using the set of time-series signals. The system then characterizes susceptibility of the inferential model to follow signal degradation. During this process, the system adds degradation to a signal in the set of time-series signals to produce a degraded signal. Next, the system uses the inferential model to perform prognostic-surveillance operations on the set of time-series signals with the degraded signal. Finally, the system characterizes susceptibility of the inferential model to follow degradation in the signal based on results of the prognostic-surveillance operations.
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