公开(公告)号：US20220261689A1

公开(公告)日：2022-08-18

申请号：US17382593

申请日：2021-07-22

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： William A. WIMSATT ,  Matthew T. GERDES ,  Kenny C. GROSS ,  Guang C. WANG

IPC: G06N20/00 ,  G06N5/04 ,  G06K9/00

Abstract: Systems, methods, and other embodiments associated with off-duty-cycle-robust machine learning for anomaly detection in assets with random downtimes are described. In one embodiment, a method includes inferring ranges of asset downtime from spikes in a numerical derivative of a time series signal for an asset; extracting an asset downtime signal from the time series signal based on the inferred ranges of asset downtime; determining that the asset downtime signal carries telemetry based on the variance of the asset downtime signal; training a first machine learning model for the asset downtime signal; detecting a first spike in the numerical derivative of the time signal that indicates a transition to asset downtime; and in response to detection of the first spike, monitoring the time series signal for anomalous activity with the trained first machine learning model.