Invention Application
- Patent Title: STAGGERED-SAMPLING TECHNIQUE FOR DETECTING SENSOR ANOMALIES IN A DYNAMIC UNIVARIATE TIME-SERIES SIGNAL
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Application No.: US17205445Application Date: 2021-03-18
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Publication No.: US20220300737A1Publication Date: 2022-09-22
- Inventor: Neelesh Kumar Shukla , Saurabh Thapliyal , Matthew T. Gerdes , Guang C. Wang , Kenny C. Gross
- 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: G06K9/00
- IPC: G06K9/00 ; G06N20/00 ; G06N5/04 ; G06K9/62

Abstract:
The disclosed embodiments provide a system that detects sensor anomalies in a univariate time-series signal. During a surveillance mode, the system receives the univariate time-series signal from a sensor in a monitored system. Next, the system performs a staggered-sampling operation on the univariate time-series signal to produce N sub-sampled time-series signals, wherein the staggered-sampling operation allocates consecutive samples from the univariate time-series signal to the N sub-sampled time-series signals in a round-robin ordering. The system then uses a trained inferential model to generate estimated values for the N sub-sampled time-series signals based on cross-correlations with other sub-sampled time-series signals. Next, the system performs an anomaly detection operation to detect incipient sensor anomalies in the univariate time-series signal based on differences between actual values and the estimated values for the N sub-sampled time-series signals. Whenever an incipient sensor anomaly is detected, the system generates a notification.
Public/Granted literature
- US12260304B2 Staggered-sampling technique for detecting sensor anomalies in a dynamic univariate time-series signal Public/Granted day:2025-03-25
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