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公开(公告)号：US12260304B2
公开(公告)日：2025-03-25
申请号：US17205445
申请日：2021-03-18
Applicant: Oracle International Corporation
Inventor： Neelesh Kumar Shukla , Saurabh Thapliyal , Matthew T. Gerdes , Guang C. Wang , Kenny C. Gross
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.
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公开(公告)号：US12086725B2
公开(公告)日：2024-09-10
申请号：US16987148
申请日：2020-08-06
Applicant: Oracle International Corporation
Inventor： Suresh Kumar Golconda , Vijayalakshmi Krishnamurthy , Someshwar Maroti Kale , Sujay Sarkhel , Nickolas Kavantzas , Mohan U. Kamath , Neelesh Kumar Shukla , Vidya Mani , Amit Vaid
Abstract: Techniques for selecting universal hyper parameters for use in a set of machine learning models across multiple computing environments include detection of a triggering condition for tuning a set of universal hyper parameters. The set of universal hyper parameters dictate configuration of the set of machine learning models that are independently executing, respectively, in the multiple computing environments. Based on the detected triggering condition, a first subset of universal hyper parameters from the set of universal hyper parameters are altered to generate a second set of universal hyper parameters. The second set of universal hyper parameters are applied to the set of machine learning models across the multiple computing environments.
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公开(公告)号：US20220300737A1
公开(公告)日：2022-09-22
申请号：US17205445
申请日：2021-03-18
Applicant: Oracle International Corporation
Inventor： Neelesh Kumar Shukla , Saurabh Thapliyal , Matthew T. Gerdes , Guang C. Wang , Kenny C. Gross
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.
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