公开(公告)号：US20230366724A1

公开(公告)日：2023-11-16

申请号：US18223079

申请日：2023-07-18

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Yixiu LIU ,  Matthew T. GERDES ,  Guang C. WANG ,  Kenny C. GROSS ,  Hariharan BALASUBRAMANIAN

IPC: G01H1/00 ,  G06N20/00 ,  G01H17/00

CPC classification number: G01H1/003 ,  G06N20/00 ,  G01H17/00 ,  G01M15/12

Abstract: Systems, methods, and other embodiments associated with autonomous discrimination of operation vibration signals are described herein. In one embodiment, a method includes automatically choosing a plurality of vibration frequencies that vary in correlation with variation of a load on a monitored device. Vibration amplitudes for the plurality of vibration frequencies are monitored for incipient failure using a machine learning model. The machine learning model is trained to expect the vibration amplitudes to be consistent with undegraded operation of the monitored device. The incipient failure is detected where vibration amplitudes are not consistent with undegraded operation of the monitored device. An alert is then transmitted to suggest maintenance to prevent the incipient failure of the monitored device.

公开(公告)号：US20230075065A1

公开(公告)日：2023-03-09

申请号：US17463742

申请日：2021-09-01

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Ikenna D. IVENSO ,  Matthew T. GERDES ,  Kenny C. GROSS ,  Guang C. WANG ,  Hariharan BALASUBRAMANIAN

IPC: G06N20/00

Abstract: Systems, methods, and other embodiments associated with passive inferencing of signal following in multivariate anomaly detection are described. In one embodiment, a method for inferencing signal following in a machine learning (ML) model includes calculating an average standard deviation of measured values of time series signals in a set of time series signals; training the ML model to predict values of the signals; predicting values of each of the signals with the trained ML model; generating a time series set of residuals between the predicted values and the measured values; calculating an average standard deviation of the sets of residuals; determining that signal following is present in the trained ML model where a ratio of the average standard deviation of measured values to the average standard deviation of the sets of residuals exceeds a threshold; and presenting an alert indicating the presence of signal following in the trained ML model.

公开(公告)号：US20250077534A1

公开(公告)日：2025-03-06

申请号：US18242291

申请日：2023-09-05

Applicant: Oracle International Corporation

Inventor： Amir Hossein REZAEIAN ,  Hariharan BALASUBRAMANIAN

IPC: G06F16/2458

Abstract: Techniques for increasing the precision of machine learning models that are trained to detect anomalous data points in a time series. The techniques including methods and systems for training machine learning models offline, using the trained machine learning models to predict anomalies in an online runtime environment, and updating anomaly detection models in the runtime and offline environments. The machine learning models may include a multitask model for predicting one or more anomalous events present in input time series data, and for each identified anomalous event type, predicting a type of machine learning model that is best suited for predicting that anomalous event type. The models may further include model instances selected using the predicted one or more anomalous events present in the input time series data and the predicted anomalous event type and used to predict an anomaly event in the input time series data.