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公开(公告)号：US20180374104A1
公开(公告)日：2018-12-27
申请号：US15633401
申请日：2017-06-26
Applicant: SAP SE
Inventor： Robert Meusel , Atreju Florian Tauschinsky , Christine Preisach
Abstract: Methods, systems, and computer-readable storage media for automatically providing a predictive model for an asset made up of multiple sub-assets with actions including receiving asset data including data values associated with the asset and at least one of sub-asset of the multiple assets, providing, by the one or more processors, a set of features based on the asset data, and executing an iterative feature selection and supervised learning process, including, for each iteration: selecting a sub-set of features from the set of features, performing supervised learning over the sub-set of features to provide a predictive model, and determining an accuracy of the predictive model, the iterations are performed until the accuracy of the predictive model exceeds a threshold accuracy.
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公开(公告)号：US11681284B2
公开(公告)日：2023-06-20
申请号：US17524195
申请日：2021-11-11
Applicant: SAP SE
Inventor： Cahit Bagdelen , Atreju Florian Tauschinsky
IPC: G05B23/02 , G06F18/214 , G06F18/21
CPC classification number: G05B23/0283 , G05B23/0224 , G06F18/214 , G06F18/217
Abstract: The present disclosure relates to computer-implemented methods, software, and systems for predicting failure event occurrence for a machine asset. Run-to-failure sequences of time series data that include an occurrence of a failure event for the machine asset are received. One or more candidate cut-off values are determined based on iterative evaluation of a plurality of potential cut-off points. A candidate cut-off value is identified as substantially corresponding to a local peak point for calculated distances between relative frequency distributions of positive and negative sub-sequences. A failure prediction model is iteratively trained to iteratively extract sets of relevant features to determine a prediction horizon for an occurrence of the failure event for the machine asset. A candidate cut-off value associated with a model of highest quality from a set of failure prediction models determined during the iterations is selected to determine the prediction horizon for the machine asset.
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公开(公告)号：US10915391B2
公开(公告)日：2021-02-09
申请号：US16455186
申请日：2019-06-27
Applicant: SAP SE
Inventor： Robert Meusel , Jaakob Kind , Atreju Florian Tauschinsky , Janick Frasch , Minji Lee , Michael Otto
IPC: G06F11/07 , G06F16/2458 , G05B23/02 , G06N7/00
Abstract: Some embodiments include reception of a time-series of a respective data value generated by each of a plurality of sensors, calculation of a regression associated with a first sensor of the plurality of sensors based on the received plurality of time-series, the regression being a function of the respective data values of the others of the plurality of data sources, reception of respective data values associated with a time from and generated by each the plurality of respective sensors, determination of a predicted value associated with the time for the first sensor based on the regression associated with the first sensor and on the respective data values associated with the time, comparison of the predicted value with the received value associated with the time and generated by the first sensor, and determination of a value indicating a likelihood of an anomaly based on the comparison.
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公开(公告)号：US10749881B2
公开(公告)日：2020-08-18
申请号：US15637471
申请日：2017-06-29
Applicant: SAP SE
Inventor： Atreju Florian Tauschinsky , Robert Meusel , Oliver Frendo
IPC: G06F11/00 , G06F12/14 , G06F12/16 , G08B23/00 , H04L29/06 , G06F17/18 , G06F9/06 , G06F16/2457 , G06K9/62 , G06F21/55
Abstract: Methods, systems, and computer-readable storage media for ranking anomaly detection algorithms, including operations of receiving a set of unlabeled data from one or more sensors in a plurality of sensors of an internet of things, generating a plurality of data distributions corresponding to the set of unlabeled data by using a plurality of anomaly detection algorithms, and ranking the plurality of anomaly detection algorithms relative to the set of unlabeled data based on a distance between a first quantile and a second quantile of each of the plurality of data distributions.
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公开(公告)号：US20230037829A1
公开(公告)日：2023-02-09
申请号：US17524195
申请日：2021-11-11
Applicant: SAP SE
Inventor： Cahit Bagdelen , Atreju Florian Tauschinsky
Abstract: The present disclosure relates to computer-implemented methods, software, and systems for predicting failure event occurrence for a machine asset. Run-to-failure sequences of time series data that include an occurrence of a failure event for the machine asset are received. One or more candidate cut-off values are determined based on iterative evaluation of a plurality of potential cut-off points. A candidate cut-off value is identified as substantially corresponding to a local peak point for calculated distances between relative frequency distributions of positive and negative sub-sequences. A failure prediction model is iteratively trained to iteratively extract sets of relevant features to determine a prediction horizon for an occurrence of the failure event for the machine asset. A candidate cut-off value associated with a model of highest quality from a set of failure prediction models determined during the iterations is selected to determine the prediction horizon for the machine asset.
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Patent Agency Ranking
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.
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公开(公告)号：US20190317848A1
公开(公告)日：2019-10-17
申请号：US16455186
申请日：2019-06-27
Applicant: SAP SE
Inventor： Robert Meusel , Jaakob Kind , Atreju Florian Tauschinsky , Janick Frasch , Minji Lee , Michael Otto
IPC: G06F11/07 , G06F16/2458 , G06N7/00 , G05B23/02
Abstract: Some embodiments include reception of a time-series of a respective data value generated by each of a plurality of sensors, calculation of a regression associated with a first sensor of the plurality of sensors based on the received plurality of time-series, the regression being a function of the respective data values of the others of the plurality of data sources, reception of respective data values associated with a time from and generated by each the plurality of respective sensors, determination of a predicted value associated with the time for the first sensor based on the regression associated with the first sensor and on the respective data values associated with the time, comparison of the predicted value with the received value associated with the time and generated by the first sensor, and determination of a value indicating a likelihood of an anomaly based on the comparison.
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公开(公告)号：US10379933B2
公开(公告)日：2019-08-13
申请号：US15463601
申请日：2017-03-20
Applicant: SAP SE
Inventor： Robert Meusel , Jaakob Kind , Atreju Florian Tauschinsky , Janick Frasch , Minji Lee , Michael Otto
IPC: G06F11/07 , G06F16/2458 , G05B23/02 , G06N7/00
Abstract: Some embodiments include reception of a time-series of a respective data value generated by each of a plurality of sensors, calculation of a regression associated with a first sensor of the plurality of sensors based on the received plurality of time-series, the regression being a function of the respective data values of the others of the plurality of data sources, reception of respective data values associated with a time from and generated by each the plurality of respective sensors, determination of a predicted value associated with the time for the first sensor based on the regression associated with the first sensor and on the respective data values associated with the time, comparison of the predicted value with the received value associated with the time and generated by the first sensor, and determination of a value indicating a likelihood of an anomaly based on the comparison.
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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.
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公开(公告)号：US10824498B2
公开(公告)日：2020-11-03
申请号：US16220438
申请日：2018-12-14
Applicant: SAP SE
Inventor： Jaakob Kind , Uta Maria Loesch , Atreju Florian Tauschinsky
Abstract: A method for multimodal failure analysis is provided herein. A multimodal failure analysis request may be received. An asset type may be determined based on the multimodal failure analysis request. Asset records for the asset type may be obtained. The asset records may include data on asset failures across multiple failure modes. A multimodal failure analytical model may be executed based on the asset records. Executing the multimodal failure analytical model may include calculating a distribution of failure intervals over time, probabilities of failure respectively associated with the failure intervals, and intervention scores respectively associated with the failure intervals. An intervention interval and an intervention score associated with the intervention interval may be selected based on the associated probabilities of failure. The selected intervention interval and intervention score may be provided in response to the multimodal failure analysis request.
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