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公开(公告)号：US20200272112A1
公开(公告)日：2020-08-27
申请号：US16284369
申请日：2019-02-25
Applicant: SAP SE
Inventor： Lukas Carullo , Patrick Brose , Kun Bao , Anubhav Bhatia , Rashmi Shetty B , Leonard Brzezinski , Lauren McMullen , Harpreet Singh , Karthik Mohan Mokashi , Simon Lee
Abstract: Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving a request to create a machine learning model for failure mode detection associated with an asset, retrieving historical notification data of the asset, generating an unsupervised machine learning model via unsupervised learning on the historical notification data, wherein the unsupervised learning comprises identifying failure topics from text included in the historical notification data and mapping the identified failure topics to a plurality of predefined failure modes for the asset, and storing the generated unsupervised machine learning model via a storage device.
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Patent Agency Ranking
公开(公告)号：US11567460B2
公开(公告)日：2023-01-31
申请号：US16284369
申请日：2019-02-25
Applicant: SAP SE
Inventor： Lukas Carullo , Patrick Brose , Kun Bao , Anubhav Bhatia , Rashmi Shetty B , Leonard Brzezinski , Lauren McMullen , Harpreet Singh , Karthik Mohan Mokashi , Simon Lee
Abstract: Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving a request to create a machine learning model for failure mode detection associated with an asset, retrieving historical notification data of the asset, generating an unsupervised machine learning model via unsupervised learning on the historical notification data, wherein the unsupervised learning comprises identifying failure topics from text included in the historical notification data and mapping the identified failure topics to a plurality of predefined failure modes for the asset, and storing the generated unsupervised machine learning model via a storage device.
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公开(公告)号：US12055902B2
公开(公告)日：2024-08-06
申请号：US18096080
申请日：2023-01-12
Applicant: SAP SE
Inventor： Lukas Carullo , Patrick Brose , Kun Bao , Anubhav Bhatia , Rashmi Shetty B , Leonard Brzezinski , Lauren McMullen , Harpreet Singh , Karthik Mohan Mokashi , Simon Lee
CPC classification number: G05B13/0265 , G06F9/542 , G06N7/01 , G06N20/20
Abstract: Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving a request to create a machine learning model for failure mode detection associated with an asset, retrieving historical notification data of the asset, generating an unsupervised machine learning model via unsupervised learning on the historical notification data, wherein the unsupervised learning comprises identifying failure topics from text included in the historical notification data and mapping the identified failure topics to a plurality of predefined failure modes for the asset, and storing the generated unsupervised machine learning model via a storage device.
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公开(公告)号：US20230168639A1
公开(公告)日：2023-06-01
申请号：US18096080
申请日：2023-01-12
Applicant: SAP SE
Inventor： Lukas Carullo , Patrick Brose , Kun Bao , Anubhav Bhatia , Rashmi Shetty B , Leonard Brzezinski , Lauren McMullen , Harpreet Singh , Karthik Mohan Mokashi , Simon Lee
CPC classification number: G05B13/0265 , G06F9/542 , G06N20/20 , G06N7/01
Abstract: Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving a request to create a machine learning model for failure mode detection associated with an asset, retrieving historical notification data of the asset, generating an unsupervised machine learning model via unsupervised learning on the historical notification data, wherein the unsupervised learning comprises identifying failure topics from text included in the historical notification data and mapping the identified failure topics to a plurality of predefined failure modes for the asset, and storing the generated unsupervised machine learning model via a storage device.
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公开(公告)号：US20200159203A1
公开(公告)日：2020-05-21
申请号：US16276876
申请日：2019-02-15
Applicant: SAP SE
Inventor： Rashmi Shetty B , Leonard Brzezinski , Lauren McMullen , Harpreet Singh , Karthik Mohan Mokashi , Simon Lee , Lukas Carullo , Martin Weiss , Patrick Brose , Anubhav Bhatia
IPC: G05B23/02
Abstract: Provided is a system and method for predicting leading indicators for predicting occurrence of an event at a target asset. Rather than rely on traditional manufacturer-defined leading indicators for an asset, the examples herein predict leading indicators for a target asset based on actual operating conditions at the target asset. Accordingly, unanticipated operating conditions can be considered. In one example, the method may include receiving operating data of a target resource, the operating data being associated with previous occurrences of an event at the target resource, predicting one or more leading indicators of the event at the target resource based on the received operating data, each leading indicator comprising a variable and a threshold value for the variable, and outputting information about the one or more predicted leading indicators of the target resource for display via a user interface.
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公开(公告)号：US11262743B2
公开(公告)日：2022-03-01
申请号：US16276876
申请日：2019-02-15
Applicant: SAP SE
Inventor： Rashmi Shetty B , Leonard Brzezinski , Lauren McMullen , Harpreet Singh , Karthik Mohan Mokashi , Simon Lee , Lukas Carullo , Martin Weiss , Patrick Brose , Anubhav Bhatia
IPC: G05B23/02
Abstract: Provided is a system and method for predicting leading indicators for predicting occurrence of an event at a target asset. Rather than rely on traditional manufacturer-defined leading indicators for an asset, the examples herein predict leading indicators for a target asset based on actual operating conditions at the target asset. Accordingly, unanticipated operating conditions can be considered. In one example, the method may include receiving operating data of a target resource, the operating data being associated with previous occurrences of an event at the target resource, predicting one or more leading indicators of the event at the target resource based on the received operating data, each leading indicator comprising a variable and a threshold value for the variable, and outputting information about the one or more predicted leading indicators of the target resource for display via a user interface.
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