公开(公告)号：US11579588B2

公开(公告)日：2023-02-14

申请号：US16049287

申请日：2018-07-30

Applicant: SAP SE

Inventor： Atreju Florian Tauschinsky ,  Stefan Kain ,  Robert Meusel

IPC: G05B19/4063 ,  G06N3/08

Abstract: Methods, systems, and computer-readable storage media for receiving a time-series of data values associated with a plurality of sensors, each sensor generating at least a portion of the time-series of a respective data value, providing a plurality of auto-regression models, each auto-regression model being provided based on a respective first sub-set of the time-series of data values used as input, and a respective second sub-set of the time-series of data values used as training data during a training process, receiving respective data values associated with a time from and generated by each of the plurality of sensors, determining respective predicted values for each of the auto-regression models, and selectively indicating that an anomaly is present in the system based on respective predicted values for each of the auto-regression models, and the respective data values associated with a time.

公开(公告)号：US20200342329A1

公开(公告)日：2020-10-29

申请号：US16394120

申请日：2019-04-25

Applicant: SAP SE

Inventor： Stefan Kain

IPC: G06N3/08 ,  G06N3/04

Abstract: Methods, systems, and computer-readable storage media for defining an autoencoder architecture including a neural network, during training of the autoencoder, recording a loss value at each iteration to provide a plurality of loss values, the autoencoder being trained using a data set that is associated with a domain, and a learning rate to provide a trained autoencoder, calculating a penalty score using at least a portion of the plurality of loss values, the penalty score being based on a loss span penalty PLS, a convergence penalty PC, and a fluctuation penalty PF, comparing the penalty score P to a threshold penalty score to affect a comparison, and selectively employing the trained autoencoder for anomaly detection within the domain based on the comparison.

公开(公告)号：US11640536B2

公开(公告)日：2023-05-02

申请号：US16394120

申请日：2019-04-25

Applicant: SAP SE

Inventor： Stefan Kain

IPC: G06N3/08 ,  G06N3/04 ,  G06N3/088

Abstract: Methods, systems, and computer-readable storage media for defining an autoencoder architecture including a neural network, during training of the autoencoder, recording a loss value at each iteration to provide a plurality of loss values, the autoencoder being trained using a data set that is associated with a domain, and a learning rate to provide a trained autoencoder, calculating a penalty score using at least a portion of the plurality of loss values, the penalty score being based on a loss span penalty PLS, a convergence penalty PC, and a fluctuation penalty PF, comparing the penalty score P to a threshold penalty score to affect a comparison, and selectively employing the trained autoencoder for anomaly detection within the domain based on the comparison.

公开(公告)号：US20200033831A1

公开(公告)日：2020-01-30

申请号：US16049287

申请日：2018-07-30

Applicant: SAP SE

Inventor： Atreju Florian Tauschinsky ,  Stefan Kain ,  Robert Meusel

IPC: G05B19/4063 ,  G06N3/08

Abstract: Methods, systems, and computer-readable storage media for receiving a time-series of data values associated with a plurality of sensors, each sensor generating at least a portion of the time-series of a respective data value, providing a plurality of auto-regression models, each auto-regression model being provided based on a respective first sub-set of the time-series of data values used as input, and a respective second sub-set of the time-series of data values used as training data during a training process, receiving respective data values associated with a time from and generated by each of the plurality of sensors, determining respective predicted values for each of the auto-regression models, and selectively indicating that an anomaly is present in the system based on respective predicted values for each of the auto-regression models, and the respective data values associated with a time.