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    PROGNOSTICS ACCELERATION FOR MACHINE LEARNING ANOMALY DETECTION
    1.
    发明申请
    PROGNOSTICS ACCELERATION FOR MACHINE LEARNING ANOMALY DETECTION 有权

    公开(公告)号：US20240402689A1

    公开(公告)日：2024-12-05

    申请号：US18203771

    申请日：2023-05-31

    Applicant: ORACLE INTERNATIONAL CORPORATION

    Inventor： Dmitriy ITKIS ,  Matthew T. GERDES ,  Kenny C. GROSS ,  Guang Chao WANG

    IPC: G05B19/418 ,  G06N20/00

    Abstract: Systems, methods, and other embodiments associated with quadratic acceleration boost of compute performance for ML prognostics are described. In one embodiment, a prognostic acceleration method includes separating time series signals into a plurality of alternative configurations of clusters based on correlations between the time series signals. Machine learning models are trained for individual clusters in the alternative configurations of clusters. One or more of the alternative configurations of clusters is determined to be viable for use in a production environment based on whether the trained machine learning models for the individual clusters satisfy an accuracy threshold and a completion time threshold. Then, one configuration is selected from the alternative configurations of clusters that were determined to be viable configurations. Production machine learning models are deployed into the production environment to detect anomalies in the time series signals based on the selected configuration.

    MERGED REFERENCE FINGERPRINT GENERATION FOR MACHINE-LEARNING DETECTION OF DEGRADED OPERATION
    2.
    发明公开
    MERGED REFERENCE FINGERPRINT GENERATION FOR MACHINE-LEARNING DETECTION OF DEGRADED OPERATION 审中-公开

    公开(公告)号：US20240344485A1

    公开(公告)日：2024-10-17

    申请号：US18133197

    申请日：2023-04-11

    Applicant: ORACLE INTERNATIONAL CORPORATION

    Inventor： Dmitriy ITKIS ,  Guang Chao WANG ,  Ruixian LIU ,  Kenny C. GROSS

    IPC: F02D41/22 ,  G06N20/20

    CPC classification number: F02D41/22 ,  G06N20/20

    Abstract: Systems, methods, and other embodiments associated with a merged-surface 3D fingerprint technique for improved prognostics for assets are described. In one embodiment, a method includes generating a set of time series signals from sensor readings of a reference device while the reference device is operated through multiple individual iterations of an exercise profile. The reference device operates in a known undegraded state. The method then separates the set of time series signals into segments that correspond to the individual iterations of the exercise profile. The method then aligns and merges the segments to generate a merged reference fingerprint. The method then trains a machine learning model to detect anomalous departures from the known undegraded state based on the merged reference fingerprint.

    EXEMPLAR SELECTION ALGORITHM FOR INCREASED DENSITY OF EXTREME VECTORS
    3.
    发明公开
    EXEMPLAR SELECTION ALGORITHM FOR INCREASED DENSITY OF EXTREME VECTORS 审中-公开

    公开(公告)号：US20240303530A1

    公开(公告)日：2024-09-12

    申请号：US18118782

    申请日：2023-03-08

    Applicant: ORACLE INTERNATIONAL CORPORATION

    Inventor： Keyang RU ,  Guang Chao WANG ,  Ruixian LIU ,  Kenny C. GROSS

    IPC: G06N20/00

    CPC classification number: G06N20/00

    Abstract: Systems, methods, and other embodiments associated with inverse-density exemplar selection for improved multivariate anomaly detection are described. In one embodiment, a method includes determining magnitudes of vectors from a set of time series readings collected from a plurality of sensors. And, the example method includes selecting exemplar vectors from the set of time series readings to train a machine learning model to detect anomalies. The exemplar vectors are selected by repetitively (i) increasing a first density of extreme vectors that are within tails of a distribution of amplitudes for the time series readings based on the magnitudes of vectors, and (ii) decreasing a second density of non-extreme vectors that are within a head of the distribution based on the magnitudes of vectors. The repetition continues until the machine learning model generates residuals within a threshold in order to reduce false or missed detection of the extreme vectors as anomalous.

    CIRCULAR-BUFFER FOR GENERATING MACHINE LEARNING ESTIMATES OF STREAMING OBSERVATIONS IN REAL TIME
    4.
    发明公开
    CIRCULAR-BUFFER FOR GENERATING MACHINE LEARNING ESTIMATES OF STREAMING OBSERVATIONS IN REAL TIME 审中-公开

    公开(公告)号：US20240256947A1

    公开(公告)日：2024-08-01

    申请号：US18104506

    申请日：2023-02-01

    Applicant: ORACLE INTERNATIONAL CORPORATION

    Inventor： Zejin DING ,  Guang Chao WANG ,  Kenny C. GROSS

    IPC: G06N20/00

    CPC classification number: G06N20/00

    Abstract: Systems, methods, and other embodiments associated with generating a stream of ML estimates from a stream of observations in real-time using a circular double buffer are described. In an example method, observations are received from the stream of observations. The observations are loaded in real time into a circular buffer. The circular buffer includes a first buffer and a second buffer that are configured together in a circular configuration. Estimates of what the observations are expected to be are generated by a machine learning model from the observations that are in the circular buffer. The generation of estimates alternates between generating the estimates from observations in the first buffer in parallel with loading the second buffer, and generating the estimates from observations in the second buffer in parallel with loading the first buffer. The estimates are written to the stream of estimates in real time upon generation.

    AUTONOMOUS DISCRIMINATION OF OPERATION VIBRATION SIGNALS
    5.
    发明公开
    AUTONOMOUS DISCRIMINATION OF OPERATION VIBRATION SIGNALS 审中-公开

    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.

    FREQUENCY-DOMAIN SIGNAL CLUSTERING
    7.
    发明申请
    FREQUENCY-DOMAIN SIGNAL CLUSTERING 有权

    公开(公告)号：US20250094830A1

    公开(公告)日：2025-03-20

    申请号：US18370101

    申请日：2023-09-19

    Applicant: ORACLE INTERNATIONAL CORPORATION

    Inventor： Keyang RU ,  Ruixian LIU ,  Kuei-Da LIAO ,  Guang Chao WANG ,  Matthew T. GERDES ,  Kenny C. GROSS

    IPC: G06N5/022

    Abstract: Systems, methods, and other embodiments associated with clustering of time series signals based on frequency domain analysis are described. In one embodiment, an example method includes accessing time series signals to be separated into clusters. The example method also includes determining similarity in the frequency domain among the time series signals. The example method further includes extracting a cluster of similar time series signals from the time series signals based on the similarity in the frequency domain. And, the example method includes training a machine learning model to detect anomalies based on the cluster.

    AUTOMATIC SIGNAL CLUSTERING WITH AMBIENT SIGNALS FOR ML ANOMALY DETECTION
    8.
    发明公开
    AUTOMATIC SIGNAL CLUSTERING WITH AMBIENT SIGNALS FOR ML ANOMALY DETECTION 审中-公开

    公开(公告)号：US20240346361A1

    公开(公告)日：2024-10-17

    申请号：US18133047

    申请日：2023-04-11

    Applicant: ORACLE INTERNATIONAL CORPORATION

    Inventor： Keyang RU ,  Kuei-Da LIAO ,  Matthew T. GERDES ,  Kenny C. GROSS ,  Guang Chao WANG ,  Ruixian LIU

    IPC: G06N20/00

    CPC classification number: G06N20/00

    Abstract: Systems, methods, and other embodiments associated with automatic clustering of signals including added ambient signals are described. In one embodiment, a method includes receiving time series signals (TSSs) associated with a plurality of machines (or components or other signal sources). The TSSs are unlabeled as to which of the machines the TSSs are associated with. The TSSs are automatically separated into a plurality of clusters corresponding to the plurality of the machines. A group of ambient TSSs is identified that overlaps more than one of the clusters. The group of the ambient TSSs is added into the one cluster of the clusters that corresponds to the one machine. A machine learning model is then trained to detect an anomaly based on the one cluster to generate a trained machine learning model that is specific to the one machine without using the TSSs not included in the one cluster.

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