-
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.
-
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.
-
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.
-
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.
-
公开(公告)号：US20210065086A1
公开(公告)日：2021-03-04
申请号：US16707539
申请日：2019-12-09
Applicant: SAP SE
Inventor： Simon Lee , Rashmi B. Shetty , Anubhav Bhatia , Patrick Brose , Martin Weiss , Lukas Carullo , Lauren McMullen , Karthik Mohan Mokashi
Abstract: Techniques for implementing and using failure curve analytics in an equipment maintenance system are disclosed. A method comprises: accessing a failure curve model for an equipment model, the failure curve model being configured to estimate lifetime failure data for the equipment model for different failure modes corresponding to different specific manners in which the equipment model is capable of failing, the lifetime failure data indicating a probability of the equipment model failing in the specific manner of the failure mode; generating first analytical data for a first failure mode of the plurality of failure modes using the failure curve model based on the first failure mode, the first analytical data indicating at least a portion of the lifetime failure data for the equipment model corresponding to the first failure mode; and causing a visualization of the first analytical data to be displayed on a computing device.
-
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.
-
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.
-
公开(公告)号：US20170180322A1
公开(公告)日：2017-06-22
申请号：US14977981
申请日：2015-12-22
Applicant: SAP SE
Inventor： Sanjeev Agarwal , Karthik Mohan Mokashi , Bhanu Mohanty
IPC: H04L29/06
CPC classification number: H04L63/0263
Abstract: Various embodiments of systems and methods to generate web application firewall specific validation rule are described herein. Initially a web service metadata is processed to retrieve a plurality of data parameters from the web service metadata. Next a common validation rule is generated based on the retrieved one or more data parameters. The common validation rule is then modified to generate the web application firewall specific validation rule.
-
-
-
-
-
-
-
Search Results
8
records
(took 0.015 seconds)
