FAILURE MODE ANALYTICS
    1.
    发明申请

    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

    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.

    PREDICTING LEADING INDICATORS OF AN EVENT
    2.
    发明申请

    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

    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.

    Predicting leading indicators of an event

    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

    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.

    Failure mode analytics
    4.
    发明授权

    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

    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.

    SYSTEM AND METHOD FOR FAILURE CURVE ANALYTICS

    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

    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.

    Failure mode analytics
    6.
    发明授权

    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

    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.

    FAILURE MODE ANALYTICS
    7.
    发明公开

    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

    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.

    GENERATING WIRELESS APPLICATION FIREWALL SPECIFIC VALIDATION RULE

    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

    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.

Patent Agency Ranking